{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e6d7246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================ Loading Hippocampus Dataset ================\n",
      "总训练样本数: 4\n",
      "各折样本数: [1, 1, 1, 1]\n",
      "训练集样本数: 3\n",
      "验证集样本数: 1\n",
      "训练集前3个样本名: ['BraTS2021_00000_t1', 'BraTS2021_00002_t1', 'BraTS2021_00003_t1']\n",
      "验证集前3个样本名: ['BraTS2021_00495_t1']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\lenovo\\.conda\\envs\\cdal_cpu\\lib\\site-packages\\monai\\utils\\deprecate_utils.py:321: FutureWarning: monai.transforms.croppad.dictionary CropForegroundd.__init__:allow_smaller: Current default value of argument `allow_smaller=True` has been deprecated since version 1.2. It will be changed to `allow_smaller=False` in version 1.5.\n",
      "  warn_deprecated(argname, msg, warning_category)\n"
     ]
    }
   ],
   "source": [
    "from brain import BrainDecathlonDataModule\n",
    "\n",
    "# 初始化数据模块（使用较小的缓存比例，避免内存问题）\n",
    "data_module = BrainDecathlonDataModule(\n",
    "    root_dir=\".\",  # 指向包含 BraTS2021_T1T2 的目录\n",
    "    fold=0,\n",
    "    batch_size=2,\n",
    "    train_patch_size=(32, 32, 32),\n",
    "    cache_rate=0.0  # 不缓存，快速测试加载逻辑\n",
    ")\n",
    "\n",
    "# 加载数据并获取划分信息\n",
    "info = data_module.setup(stage=\"fit\")\n",
    "\n",
    "# 打印关键信息，验证数据加载是否成功\n",
    "print(f\"训练集样本数: {len(data_module.trainset)}\")\n",
    "print(f\"验证集样本数: {len(data_module.valset)}\")\n",
    "print(f\"训练集前3个样本名: {info['train'][:3]}\")\n",
    "print(f\"验证集前3个样本名: {info['val'][:3]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7c29bcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 形状验证 ===\n",
      "T1形状: torch.Size([1, 32, 32, 32]) → 预期 (1, 32, 32, 32)\n",
      "T2形状: torch.Size([1, 32, 32, 32]) → 预期 (1, 32, 32, 32)\n",
      "\n",
      "=== 强度归一化验证 ===\n",
      "T1值范围: [0.00, 0.60] → 预期 [0.0, 1.0]\n",
      "T2值范围: [0.00, 0.82] → 预期 [0.0, 1.0]\n",
      "\n",
      "=== 空间对齐验证 ===\n",
      "T1-T2有效区域重叠度: 0.96 → 预期 ≥0.95\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 确保trainset已初始化（前提：先执行 data_module.setup(stage=\"fit\")）\n",
    "if hasattr(data_module, \"trainset\") and len(data_module.trainset) > 0:\n",
    "    train_sample = data_module.trainset[0]\n",
    "    t1_img = train_sample[\"t1\"]\n",
    "    t2_img = train_sample[\"t2\"]\n",
    "\n",
    "    # 1. 验证形状\n",
    "    print(f\"\\n=== 形状验证 ===\")\n",
    "    print(f\"T1形状: {t1_img.shape} → 预期 (1, 32, 32, 32)\")\n",
    "    print(f\"T2形状: {t2_img.shape} → 预期 (1, 32, 32, 32)\")\n",
    "    # 额外判断：若形状不符，提示可能的原因\n",
    "    if t1_img.shape != (1, 32, 32, 32):\n",
    "        print(\"⚠️ T1形状不符！请检查：1. train_patch_size是否为(32,32,32)；2. RandSpatialCrop是否生效\")\n",
    "\n",
    "    # 2. 验证强度归一化\n",
    "    print(f\"\\n=== 强度归一化验证 ===\")\n",
    "    t1_min, t1_max = t1_img.min().item(), t1_img.max().item()\n",
    "    t2_min, t2_max = t2_img.min().item(), t2_img.max().item()\n",
    "    print(f\"T1值范围: [{t1_min:.2f}, {t1_max:.2f}] → 预期 [0.0, 1.0]\")\n",
    "    print(f\"T2值范围: [{t2_min:.2f}, {t2_max:.2f}] → 预期 [0.0, 1.0]\")\n",
    "    if not (0.0 - 1e-5 <= t1_min <= t1_max <= 1.0 + 1e-5):\n",
    "        print(\"⚠️ T1未归一化！请检查ScaleIntensityd是否添加到train_transforms\")\n",
    "\n",
    "    # 3. 验证空间重叠度（修复除以零问题）\n",
    "    print(f\"\\n=== 空间对齐验证 ===\")\n",
    "    t1_nonzero = (t1_img > 0.1).numpy()\n",
    "    t2_nonzero = (t2_img > 0.1).numpy()\n",
    "    t1_nonzero_count = t1_nonzero.sum()\n",
    "    \n",
    "    if t1_nonzero_count == 0:\n",
    "        print(\"⚠️ T1无有效区域（全背景），无法计算重叠度！请检查样本质量或调整有效区域阈值（如0.05）\")\n",
    "    else:\n",
    "        overlap = np.logical_and(t1_nonzero, t2_nonzero).sum() / t1_nonzero_count\n",
    "        print(f\"T1-T2有效区域重叠度: {overlap:.2f} → 预期 ≥0.95\")\n",
    "        if overlap < 0.95:\n",
    "            print(\"⚠️ T1-T2空间对齐不佳！请检查RandCropd是否对t1和t2使用同一cropper\")\n",
    "else:\n",
    "    print(\"❌ trainset未初始化或为空！请先执行 data_module.setup(stage='fit') 并确保数据加载成功\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daef8440",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "import numpy as np\n",
    "\n",
    "# 1. 手动指定一个图像文件路径（从dataset.json中复制一个t1路径）\n",
    "# 示例：假设dataset.json中\"t1\"路径为\"imagesTr/BraTS2021_00000_t1.nii.gz\"\n",
    "t1_path = \"./BraTS2021_T1T2/imagesTr/BraTS2021_00000_t1.nii.gz\"\n",
    "t2_path = \"./BraTS2021_T1T2/imagesTr/BraTS2021_00000_t2.nii.gz\"\n",
    "\n",
    "# 2. 用nibabel读取图像（跳过所有预处理，直接读原始数据）\n",
    "def check_image_file(path, name):\n",
    "    try:\n",
    "        img = nib.load(path)\n",
    "        img_data = img.get_fdata()  # 转为numpy数组（原始像素值）\n",
    "        print(f\"\\n=== {name} 文件验证 ===\")\n",
    "        print(f\"文件路径: {path}\")\n",
    "        print(f\"原始数据形状: {img_data.shape}\")\n",
    "        print(f\"原始数据值范围: [{img_data.min():.2f}, {img_data.max():.2f}]\")\n",
    "        print(f\"非零像素数量: {np.count_nonzero(img_data)}\")\n",
    "        \n",
    "        if img_data.max() == 0:\n",
    "            print(f\"❌ {name} 文件全为0！可能是文件损坏或下载不完整\")\n",
    "        else:\n",
    "            print(f\"✅ {name} 文件正常（存在非零像素）\")\n",
    "        return img_data\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 读取{name}文件失败: {str(e)}\")\n",
    "        return None\n",
    "\n",
    "# 验证T1和T2文件\n",
    "t1_raw = check_image_file(t1_path, \"T1\")\n",
    "t2_raw = check_image_file(t2_path, \"T2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8dfad64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "批次T1形状: torch.Size([2, 1, 32, 32, 32]) → 预期 (2, 1, 32, 32, 32)\n",
      "批次T2形状: torch.Size([2, 1, 32, 32, 32]) → 预期 (2, 1, 32, 32, 32)\n",
      "T1数据类型: <class 'monai.data.meta_tensor.MetaTensor'> → 预期 torch.Tensor\n"
     ]
    }
   ],
   "source": [
    "# 获取训练集加载器\n",
    "train_loader = data_module.train_dataloader()\n",
    "\n",
    "# 迭代一个批次\n",
    "for batch in train_loader:\n",
    "    batch_t1 = batch[\"t1\"]  # 批次T1\n",
    "    batch_t2 = batch[\"t2\"]  # 批次T2\n",
    "    \n",
    "    # 验证批次形状\n",
    "    print(f\"批次T1形状: {batch_t1.shape} → 预期 (2, 1, 32, 32, 32)\")  # batch_size=2\n",
    "    print(f\"批次T2形状: {batch_t2.shape} → 预期 (2, 1, 32, 32, 32)\")\n",
    "    \n",
    "    # 验证数据类型（需为torch.Tensor）\n",
    "    print(f\"T1数据类型: {type(batch_t1)} → 预期 torch.Tensor\")\n",
    "    break  # 只测试第一个批次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af17ae4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 取训练样本的中间切片（3D图像的第16层）\n",
    "slice_idx = 16\n",
    "t1_slice = t1_img[0, slice_idx, :, :].numpy()  # 移除通道维度\n",
    "t2_slice = t2_img[0, slice_idx, :, :].numpy()\n",
    "\n",
    "# 可视化\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(121)\n",
    "plt.imshow(t1_slice, cmap=\"gray\")\n",
    "plt.title(\"T1 Image (Slice {})\".format(slice_idx))\n",
    "plt.subplot(122)\n",
    "plt.imshow(t2_slice, cmap=\"gray\")\n",
    "plt.title(\"T2 Image (Slice {})\".format(slice_idx))\n",
    "plt.suptitle(\"预处理后T1-T2切片对比（应空间对齐）\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "421780c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集批次T1形状: torch.Size([1, 1, 240, 240, 155]) → 预期 (1, 1, H, W, D)\n"
     ]
    }
   ],
   "source": [
    "# 测试测试集加载器\n",
    "test_loader = data_module.test_dataloader()\n",
    "test_batch = next(iter(test_loader))\n",
    "print(f\"测试集批次T1形状: {test_batch['t1'].shape} → 预期 (1, 1, H, W, D)\")  # 原始尺寸，未裁剪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7791ddbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状： (240, 240, 155)\n",
      "像素间距（mm）： (1.0, 1.0, 1.0)\n",
      "影像维度： (240, 240, 155)\n",
      "空间变换矩阵：\n",
      " [[ -1.  -0.  -0.   0.]\n",
      " [ -0.  -1.  -0. 239.]\n",
      " [  0.   0.   1.   0.]\n",
      " [  0.   0.   0.   1.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nibabel as nib  # 导入nibabel库\n",
    "\n",
    "# 1. 加载nii文件（支持.nii和.nii.gz格式）\n",
    "file_path = \"BraTS2021_T1T2/imagesTr/BraTS2021_00000_t1.nii.gz\"  # 文件路径（替换为你的实际路径）\n",
    "nii_img = nib.load(file_path)  # 返回一个Nifti1Image对象，包含影像数据和头信息\n",
    "\n",
    "\n",
    "# 2. 提取影像数据（像素值）\n",
    "# get_fdata() 返回numpy数组（float64类型），支持3D/4D影像（如3D体素或带时间序列的4D数据）\n",
    "img_data = nii_img.get_fdata()  \n",
    "# 查看数据形状（例如3D影像可能是 (240, 240, 155)，对应x、y、z轴的体素数量）\n",
    "print(\"数据形状：\", img_data.shape)  \n",
    "\n",
    "\n",
    "# 3. 提取头文件信息（元数据）\n",
    "# 头文件包含影像的物理信息，如像素间距（mm）、坐标系、维度等\n",
    "header = nii_img.header  \n",
    "# 查看关键信息（例如像素间距）\n",
    "print(\"像素间距（mm）：\", header.get_zooms())  # 输出 (x间距, y间距, z间距)\n",
    "print(\"影像维度：\", header.get_data_shape())  # 与img_data.shape一致\n",
    "\n",
    "\n",
    "# 4. （可选）查看 affine矩阵（空间变换矩阵，描述影像体素与物理空间的映射关系）\n",
    "affine = nii_img.affine  \n",
    "print(\"空间变换矩阵：\\n\", affine)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设是3D影像，取z轴第80层（可根据实际shape调整索引）\n",
    "slice_idx = 80  \n",
    "slice_data = img_data[:, :, slice_idx]  # x-y平面的切片\n",
    "\n",
    "plt.imshow(slice_data, cmap=\"gray\")  # 医学影像常用灰度图\n",
    "plt.axis(\"off\")  # 关闭坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7a88614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状： (240, 240, 155)\n",
      "像素间距（mm）： (1.0, 1.0, 1.0)\n",
      "影像维度： (240, 240, 155)\n",
      "空间变换矩阵：\n",
      " [[ -1.  -0.  -0.   0.]\n",
      " [ -0.  -1.  -0. 239.]\n",
      " [  0.   0.   1.   0.]\n",
      " [  0.   0.   0.   1.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ddDodDIdDE670HljTMB6PkclkTLjynCpgKdjZqyifz5tApxJkUDuZTNr9Kh2lCkIzl5bLJTKZjKXMMnhMuov3SSXpttMIaqPBa6niSqVSKJVK2Nrawv7+vhX7McOL1d+MgwRlVXn8/sErBY83xsbGBvb397G9vY3NzU3cunULpVIJ7XYbT548wfPnzzEYDKzNM/caoJAMh8OYTCbodrt4/vw5er2eNZ+jVa59gQhW6wJX1jEbwV1cXKBQKJjQHQ6HGAwGa5vbqNVMMO4ArNM85+fnpjAoXKPRKD755BNsbGxgd3fXhD/TOUnZ0AOhcCXHn8vlUK1WUavVMJ/P0el0LCtqe3sb1WoV4XAYp6enmM1mlpGkVrxmHTG2sLOzg1u3buHTTz81ao5eC+k1Uj5K/bDKWufF3dWNCqRSqSCfz6NYLGJ/fx+3bt3CH/3RH6Fer+Pg4ABffvklvvzyS/T7fZt3Uloev7/wSuEtBIXWarUy/j6XyyGXy+Gdd95BPB7HarXC2dkZbt26hY2NDZTLZSuMGo1Glt9+enqK8/NzPHr0CMB10BO4zp4hGKyNRqNot9vWVG42m1ngWOMBFMxUFDwvx84U0mQyiZ2dHTQaDctcIn8/HA4BrG+HyXMoV79cLjEajcz70KycarWKUqlkgdxQKIRarYZsNot4PG4KizEK7WtE6qlcLtvv29vbWK1WGAwG6PV6GA6HaLfbRj3prm1UOOyzxAypnZ0dlMtl69tEhcBK7fl8jkQiYWNWheN2bHUb79HzKZfLGA6HePr0Kc7OzrC1tYXNzU189NFH2N3dxb1791AoFCwexNYZo9HIWpOn02m02217lh6/+/BK4S2A5q5TaLAojEKmXC6jVCrhwYMHxu3HYjHcv3/fPAK2iwZgPPVsNjPhxpbOwLVFqwKHwobFVeTQqZxCoZC1p1BrX2kUtWjH4zEmkwmm0ymy2ay1sCaNpI3l1Fsh3KpjNp3r9XrWcA64ps14D6FQyOomGL8geE2XPspkMpa+WiwWbX4uLy/R6/Xsvul5uKAXEo1G7dqRSMR6NVG58lh6KswicueO0HoJnWMG7elVtNtti8Ew/lAsFlEoFIy2CoVCGI/H9jxZhMhgvvcgfj/glcI3HExJpAWXyWSMJ75z5w7K5bKlj9ITaDab6PV6qFQqSKVSWCwWePLkCer1OprNJorFInZ3d1Gr1TAej9Fut9faOY9GI2u/wPbTwLpQYvuJ6XSKSqViSqHb7dpnk8nk2laVwJVQZsB1uVza8Q8ePDBL/csvv8R0OsVsNrPgND/rZt0AMEucQeder2f0l7aeprKKRCLIZrMoFArI5XKWEQUA2Wx2bf4zmQx2dnZQqVRMON+6dQuTyQSXl5cYDAbodrs2Jzo+d4yMQ5RKJSwWCzQaDTx9+hT9ft/iB1RmVBr0+KgoWXhHIa33RaXJmAdjCPF43K5HJcSMrbOzMywWC2xsbKBYLGI8HqPT6aDZbFp7kVwuh1QqhcvLy1//Avf4tcMrhW8gIpEItra2zIqORqPI5XLIZDK4d+8eqtUqdnZ2sLe3h0gkgslkguPjY6MyGBcArnYOi8fjGAwGa5Y5BQYtQyoPChitNlYqRGkbCqTpdGrWONtUsyDMzZFXzjoej9uxl5eXSKfTqNVqGAwGlgrL63Ac2qmU/wNe5e2Ve2f7B838SSQSphhSqRRCoZC10+B91Wo1FItFVCoVu2Y+n7e4CJvzuWmoFOYcl2YzcbtObgLU6XQs8M5sKwaLXYpIlY4W92nxnQb8XdqJx3BeqYj4TKlUSBsNh0O0Wi1Mp1MLzpPSqtfrPiD9OwqvFL5hYEomLf/xeIx0Oo1SqYRqtYp79+6hUqlgc3MTe3t7mM/nODs7Q6PRwMXFBbrdrlmKiUTCrFjlhJmlwoIoANYqQVs3aBEVABM+Ss0wXZSeBq1SbbGg9AypDX6eQqvb7RoVxhRNFmpp5hKFlhaE8Rrk8/lFgclrKJWl6bVUiG6r7GQyiUwmY7ESABaLAa5aaDDzyi1Ic9tecIxUcFQmnHN6AVqHwful0tLNduh5qIKmUuDzI5W1XC6NAgKwtqsbP0/FT4qJPaRWq5V1meXWp5ot5tNaf/fglcI3DNVqFdlsFs1m0/LYP/nkExNedP8fP36Mb33rWxb0PTw8RLfbRS6XM+GgWSvkkoEr72E0GqFer+PBgweIRqPY3d3F8+fPLUWRgpRBa8Jt3UC6gtw8ewONx+O1AjZSSPRAeA6eu9PpIJ/PI5lM4v3337eA8fHxMVarFSqVCsrlMhKJBPr9vhWesaUDEBxjYL0Ci9uokBKJBCqVCgaDAS4vL00oatCZezyTRmHjP1JG3K6T90hlqRQPx0FFQ+HP4y4vLy3ewQAvcNUzqlwuo1gs4t69ezg7O8PR0ZFRP9rWmwp4tVpZbIPjodehO8cxS8xNKND4DT/Dnex6vR6Oj48xm80QiUSwt7eHVquFVqv1qy96j18rvFL4hiAajaJSqQC4skCTySTK5TLK5TJWq5XxvNpIrtFoIJVKAbi2KnWfAAUrbzUgOR6PMRgMEI1GAz8DYE0AMfirngLPTQ68UCggGo1a6iY9C1YOK/etDe+m0yk6nQ6Oj4+t+IwFX6vVCrlcDpFIxKxr7WtE61p7DtGDYXB9PB6vBZVJkbDOQO9Jg+YU/sPh0PaG0HbVFP7aDVVTRdVDUfqIynQ8HpvCJt3DeAz3m97e3rYEAb0/eimaFECoB+F6fFQY9HiC+j9pE0B6rfP5HO122wrjQqHQ2rPy+N2AVwrfEESjURSLRROm1WrVlAIrdkkNEa1WC8vlEul0ei2w6hZRAdfFTQxmAteWNPcldl9sFWRUGlQqzNKhcKNgzGazliGlW1pq1TIF+Xw+t7FQ2J+enqJYLNp9ZDIZqxegEGX6Ji1aVXS0enktdmQl/cH5Y1U3505jEpwnKk0GXbV4j96WUiha/KUKgnPLzzPllkIduFbqVMLMUmKGkNI/vDetVeB53IpqfY46Nj5DKmd6M256KwBrdU5FREVLCo5z4xXD7wa8UvgGgDzt8fExdnd3sbW1he9+97u2E9fLly8BwALOpIYuLi4wn8+RzWaRyWSsBz+FMGkMCgoKYH15mVFCIUPhQKGsGUjaAoJKgDw+vYl79+5hsVggl8vh8ePH6Ha7WCwW1qaB7S9UwTBg2u/3rUdSNpvFxsYG7t69i0gkglarhc8++wz1et2EJj0H4DoWA2BNIHNO6ClQAKq1rA38ND7AeAzHxFTUeDxu3UrduVXhquD1gGuKinQbW2WUy2VTeNVq1aqoP//8czx8+BDPnj0zJUTFwOfA56pKl39XhQnAsrrYnVWVmVY3a0Gf9mDa3NzEkydPLF5TKpXWlLXHbxdeKfwegwKX6ZTVahW3b99GrVYDAFMKwDWlwbYTpBrIBTM4qG0gNAjoWqy8vu5CpoKMVqnSE2rZa0BX9wVIp9PW2rrZbAKA9fhn/r3bUoFN4Tgucu4s6lIFR4WgmTZaiayFYgAs955WvQpw9jXSAK6C+0KsVitTZuVyGfl83pSR24KCc6f0S9Cz4Byw8LBYLKJWq6HZbGI2m2Fzc9NqI54/f2670emcuc+KoLJ16zo0hsDfSZ1pgJ3rgi1HlKKj95rNZi3FWak+LV70+O3AK4XfY3AnLRZT3b9/H3t7e8jn8+h0Ouh0Omi1WkgkEvYZKoVoNGpKAbguRgOuBbZbV6CCmULD5aNd6sPdy5iKgf/T3cRYhcvWFQxMMjjKe+Z4aJlSsGsrDZ5fs7BI/Wguvt6HVnqrB0FLn1w+r0WlwLlyhSuD2AxoRyIRy5JSIc/MIKVtqJzVs9KfKXDZmJAJBZeXlxiPx9Z0sN/v49mzZzaHpJhUKeiz43g0zVTHydRbKs9CoYDRaGQN/vgZxhj4OY67VquhVCrh/v37ePz4MR4/foxms2mBee1465XDbwdeKfyegpZ9MpnEnTt3UCgUkEwmcXJygsPDQ6sWplXMfkEq2Ck0uJ8B3XcKI8YY1DJVIUGqRNMZ3c6bPI8L0jdUIgzqPn36FKlUCtPp1Iq7KMgArAnfoDjAanXdbXUymeCLL76w+2FRnXssfyaVQq+D90uPq9VqYWNjw2ILyWTSegM1Go01pcN5p5DkXg3MAtPd21S5Atf7NVMxcGwUlPwcOf1ut2sU4Pb2tmWUPX78GCcnJzg/Pwdw5dmMRqM1Y4DQNFT32VER67NjM7+trS3zgpgey3gRCxR57cVigXa7jeVyifv37+Pdd9+1+E+73Uav17MYTTKZXPNuPH5z8Erh9xBaIFSr1UwhsEKYwll7+wRx1Az8tdttS9NUS59WuFbZuha+ctRu6qkqDM1I4bkICkVWwzKrSOMRKsR4Xj2P3iM/u1xeVVdr3MENpOs4NJiuefcUbhcXF9YlFrgONhcKhbWUXX5OhaP2NEqn05hOp0gkEmuejioGjscN8ur5OT9siFev162YrtFooN1u4/Ly0q6vipvPSOeLf6Oy0XYZVEikrLjvgsaD3PFxznkMg+P9ft88Grb9IDQgT8/VK4bfLLxS+D0DhQ17z7z77rtm9TcaDXuBSVEoVUDrVWmc6XSKer2OwWCwRhtRAbjFThpI1TYQ2oANWI8Z8EtpLFq+Kji5p8F8PreaA8YLlIPnlyvgXQVEQUvlxWM0A0srcnl+VmvzPIvFAr1eD8+fP7csL+7bnMlkrKW2NuyjYOV8stYhHA6jWCwa5cdAOu+V9wSsZ/2oIuTvLNbj3spPnz61liXcm/no6Mg8D00d1fOpEtQ5VqWg1+aeE8lk0rKzaFDo+Ny5Z6CeioHHc7vQZDKJ58+fWy8lJlB0Op1f+D3x+OXhlcLvAVgBSuuyWCziz//8z1GtVpFIJPDll1+i1WpZgHa1Wq0JeaUjtEcOs5AoVKlwNGCoOfzqNbD9A61QpRlU6Kiw1ToDAFaUxrRGtqtgqw16L+oBqcdAgaXpljwnr6lBcW234XZzZV0Bj2cPo8XiapvO5XKJk5MTNBoNZLNZ7O/v4/79+8hkMrh16xZevHiBer1usQO9TyqwyWSC8/Nz3Lp1CwBQqVQsC0mFslrdbrqsUkh8llRIx8fHOD4+NjqGxWGabeRSUHwmfI5MTdZnpV5eLBZDpVJBsVhENpvF5eUlms0mzs7O1u7drdJmbEWVsHpG8XgcxWIRt27dQq/XQ71etxgF98xWZe7x9cErhd9BsAqYQpieQaFQQKFQwM7ODu7du4dMJoNWq2UcMbnwoBx316rWzKGgoLF+uVlHajXyd9eSVahwINQT0ZgD91qmENEU0JvOr7SNSwfddLxmLyk1wjnSMXH89MbouWQyGWxublp77UqlgsvLy1eC9AQ/32g0zJvT4HHQmF2uX8dPRUcPbDgc4uTkBNPpFK1Wy7h+jckEQWM0em73uVGgk/fnNQeDwVobFPcetDhP511BhZTP59fOq4aNGiweXx+8UvgdQzgcRj6fN0qGTc7y+Tw++OAD3L59Gx999BE2NjbQ7/fx/Plzs6CSyaRtf8nWByyWAq5fRKV8IpGIVTXTgqRwVC5XlYW+2MpFMxYQdE9u1hAFj2b+hEIhDAYDjEYjdDqdNcpKqRXNZiFUyKjVrVSX8vrAtQVPxURB97oUzePjY/T7fTx69AiLxQIffPABPvnkE+zv72M8HuPp06drQX2OjWmxBwcHyOVyyGaza3Qb79H1Elar1VomEOeNFn04HEahUMByucTR0RE+++wzxONxJBIJDIfDNUor6NlQ6NLL1OC/7gXNVuqslGZ8gAkKpA81jqTxJz47XVP01nR+WWgHAEdHR7YWUqkUYrGYpVh7fH3wSuF3BJoFw9YFhULB9sjd29vDnTt3kM1mrTqX2TR8+dhXhlQTPQi1+Mhr828MNhMu5aNwLWyFCltgnXqiUqCFzUpatc45Jq3QpSBxlYJr3eu4XK6cgldjDBROmmar3tR8frX5D9M9qfCi0ShqtRpyuRwmk4m1ri4Wi7bndKlUQqvVsgA350bbfff7fUQiEVQqFUvD1ACvNpzT2grenz4bCtl4PI5qtYpms4lms4nl8qqDKpUK1wKfW1AnVtJGLErjeqFgz2QytlmQ9m/SimSuJzc2wrHq+qEBQgXHmFcqlcKtW7ewtbWFbreLx48f25phOxQAvtjta4JXCr8l0CqjlczumSwIYofNSqWCUqmEXC5nQlPdavaq58vL4K2bjUPl4eaea7qp/g1Y73WjikVpHI0jBFmiQZ9xs5Lc46gQeF43zVW5er0fl2rhfNKy5njpKSkd5mY0jcfjNWucii2fzyOTyZhC7vf7ODs7M+HM50hBroF2jo1Kn8HieDyO0Wh0I82msZGgTDD3ubNmgJSOK5yDag/0efJeNRmAQXWm07KxopsAEHQ9pbA4l26mmh7Ld4BK8+TkxFq0qEHD9+cmmtDjl4NXCr8lRCIRFItFqycolUrY3NxEuVxGoVBYa/zWarVwenpqLn2hUMD777+PQqGATz/9FPP5HEdHRzg+PjY3ezKZmFDTzpfs9aOcPgUYlY7SNBTOTLkMh8PmWdDKA9bjF8DN/Xs0puGmhgYJaQoJpXyUdgji4FUZsEJWM6+0zYUWulEYhkIhtNttrFYr24eCNSFMkY1EIsalf/7550ilUmvWts4vz0/0ej2EQiFTDuxeS4Gnn1c6R+k/KoP5fI5ms2nPj8ZBr9ezeaYX6fL+FORB8RN6R+l0GqlUCltbW7YJ0/HxsVWLu+m77vMPijfxmnqv/Nt4PDavaXNzE8ViEefn5wiFQmv9okKh0NreGx6/Pnil8BsEd82ipT+bzbCxsYF8Po+7d+9aCh67clJAUwiw0+R8PsejR49QLpdx584dq2htNBpYLpdWMKQWdFCFqiuA1GvgcfryAteN027yCqg4tE6C16L3wpc+SCCp8ODPDFTyeLXeFRT4nCOOO51OY29vD8Ph0PZhVmuTfZW0+prFWbVaDbdu3UI2m0U+nzchNBgMcHJygmaziW63i62tLeTzeWuep0rOFXyLxVWjvWazidVq9UpDPT5n14rX+VVFwUZ/0+kUvV7P9mlQi9+tVXGzmnSHPBoPu7u7RnsNBgNTLlodrc9KYwl6La4ZekVMiOCa5Jrjushms9jZ2cG7775rCQf37t1Dv9/Hy5cvcXJyYntacz1wzXv86vBK4TcIBnVpsSaTSfMONjc31+gApmiSbybNxBe8Xq9jNpuhUCggHL5qEcACNhWsmh4alKWjSkERFFdwjwuiOzQgqtcJ+rzOi4sgD0Dz/zUoSrhWKa3JQqGAzc1N2+3LjUWQXqIgjkQi1k+IlcKM5XC3t/n8eu/ier1ugWMqc/deNC5APn00Gr2i4HgPmtKrlBHHzLlkSi9rIVix7K4XN0jvKhs3BsQUUVI0rVbLKCltAcJxUKi7mWouVaXpuvoclfJLJpMolUq27WilUrHai/l8jm63i8FgsGYsePz64JXCbxB8iTc2NlCtVvHxxx9bdSx3P+v1emap0qpi0ZEKB2Z9jEYjVKtVRKNRlEolANd7/WrDt1AotMY/aywgKEbA8fIY4PoFZ6YKoQpEKSHXGtVGc66AcnPmNQ8/FotZwFi9GZezp3BhC5BUKoU7d+6gUqlgf38fjx8/NqVAao3jZZC+VCqhVqthf3/frOQXL16YotjY2EAikUCpVMJ8frU/wNnZGcrlMpLJJLrdrnl1WhsCwIK9LIxjWwcqLwZTg7wl/qzWPwvi+D9tX+EqIPd58LmR8tLAMusQyuWy1SG8fPlyLetJqTj+7CoCKguuF03B1fVHZc/2GdzClAH/fr9vHlg+n8fe3h5SqRQODw/XYkMevx54pfAbQrFYRLFYxN7eHm7duoVKpYK9vT3jZrvd7lp7BwBW8EShogKbVhpbXScSCWvQxkpTYJ3u4ctMQaWUhvti6zU0SKpWrVq3vAYtRj1Gz61/188HKQn+THCspFsWi4Xd9+bmpikD7iNNSzyVSmFzcxP9fh/L5RLPnj1by34htra2sLGxgZ2dHSyXS6spID9PC5ZtuWOxGLa2tpBKpbCxsWEKPkgxarYXldp8PrfUVT6TIAubcAv3VHG6MQxeS79uOi+PjcViyGQyKJVKSKfT1u+J6cF8Ju6e0m68iGPl/9XDo4FCyo4/U6Fz+9LZbGb7hl9eXq7FEqg4BoOB7Yznzp/HLw+vFL5mUMCVy2XUajVLLS0Wi8jlcmi325YtQmHBl9wNDrpVsqvVCqPRyAREPp+3bRDdnbD4Ob64rC7leYOKmzSbhwJdd9wCggvX1HNQ6orfNdAIrG/woudQZcTPsecTO59yL+C9vT0LBLO5GneZC4evaj82NjYsdZR9d+hJhcNhe0aVSgXHx8doNps4PDy0TKNw+GoXsXw+b/dNJcG5dwusVCHwHjVzSA0AF+68qtWvz1Qzd/TLVfRBdJ/+ndlVnN9ms2nzqGvP7bDqUoTufbrUH40YKgIqCADWOn2xWKDZbKLT6dgYmDCg6bEcj5ud5fHLwyuFrxnZbBabm5v4wz/8Q5RKJRQKBQBXewTQNedWibR+tdhMXy632yj/x20dmVmUTCbNcnL7GGkrB55DvQn24CFnTjDzhJwvcN1HiIJA22O4XgEAUyhaSMaWExT6tEJ1+016BrQkd3d3rV6AnsJwOESz2cSzZ88s2Mo8fTaN29/fx8bGBgaDAR4+fIiTkxP0ej3z4srlMpbLJU5PT/Ho0SMTSEwHXi6XlgFDBUGKj4q8Vquh1Wqh1+utdZnlXLJmgPUaGmtws7RU8PP5uIpUW2SoAlDloeuGikjTbIHr1umJRMK2Kz07OzNaDoB5YCxq0/PputEW2kzR5bVyuZxtY8p7VnqUz3M+nxtFyuAzDSXOUblcRjweRz6fx+HhoY3JZyP9avBK4WtEsVi03b9SqRQWiwXOz8/XuFwAa1thAq8G8FSwqAtO8KXihuoA1pQCf1dO+ia4lp9a6EqB8JyqANzP6jFuQFHjAlrtrN4Jv1xLlI3gNjc3MZlMMBqNLBOIO6tRWPX7fTQaDRweHmJ/fx+JRAL3798HABQKBZydnaFYLFpKMDu1amtrUiEUSOFw2BQxFRkVlG77yeehgWMV9JrtxHhJUEKA/u5axByfHq/f3Z/5WY3/qMfBRnXaZ4q/U0HoGDQbiseoZ6cBZa5zKlHODeM5jLfoLnTMPlKaSr1fFrOVSiUzBvjOvK61h8fN8Erha0IoFEK5XMb29jbu3r1rVM/l5aVZZfl83lIi2bEUuObhyTsD18FjtZx5Hc1mYTWqxg40M0TpG+Wf3TYUBF9ctTZdmodjUEUWxF0rBaHphAzKkuqh4FFhSjB9MpvNWiC01+vh+PgY7XYb3W7XApukmBqNBg4ODrCxsYFsNot79+4hFovZDmDcxzifz5v3RmGtu4qNx2PzwrjP8GJx1VZ7NBohn8+jVqthOp1agBSAWdEUVhrDcRMIOKf0GoNiATofNwk+VSZBlAoVghoIrFSmcHdjBByHejLaGkQND9ewUC8xKL2YHhQVBq/NuAmtf5dWJF1aLBaxWq2MbtVGjx6/GLxS+JoQDofxwQcfoFQqYbVa4cWLF2a9Uoj3+30T4ul02gSjtjnguRgn4ELXTpbA9QsNwJQMX3qX46YwovBW+kEFk1pzylVTSZD7peJhIz/1TvQayh0DsAwhVsmSMlKlxbHw2qQmKGjPz8/x6NEjXF5emsVI/pmCSmsCksmkVeey06nOM+eLSqbT6awFQjkO3X0sl8uhUCigVCphf38f1WoVm5ub+MlPfmIKIyhwrq3H1WLXrCUVoHxGKuyC4jkay3Gh41BPgUJXA7bai0mFvl5L1xbnT40Zjll7cHFDpXw+b3QSY1xUxqHQVX2J0pgcgxuHYcsQ/u3i4sJTSL8CvFL4mkBPIZFIWIk+OVMuZl3kfKHcVEY3w0ctPNcS1Bf3puCjmynEzym9wf+rm68WIAWPZjEB1/s1qOegQUaOh/+jBcgN7WmV8zvvnZu58NrMsCKlpr13aI0D120QWE9AeiESiVgXTu5nQIURDodRqVRw+/Zti1Wol8Wf9X4AmEKu1+umINnymZ1sOa9BNJv7rPV5U8iqUgJeDRxrhpf7eXdt6ncqAKWx1Btw145eV4sE3ViVnp+ejx6vu7IRqlQYw3C9KfVaOC52WCW1RyPDrRnx+Gp4pfA1QDOOKJC0QpXC2211zQAehaUKD7rXKsDdrBae1+Vf+dJzDEEvvxtnoGDToLBLC6nA4BgBrPVe0vOop6MvN+eBwoHj43iZZko6hx4G9z4m58x5Uyuenle9Xkej0UChUMBqtUKj0UCn08HJyYlZxCySqtVqdo7z83PbZpK0hvLnAEyAcatJJhTs7OzYFpgUgIlEYi2Ar4LepQwJ10Lnce6z4NpwFY7CVRL6HJQmdDOd9PpufYgq/5uynehdaWU96w9IpzI+QEXOlFMW6HGu9X3hmk8kErY1arfbRa/XsxTkII/J42Z4pfA14MMPP8R3v/tdUwjD4RDJZBKr1dXmN8xYAa4XuXK4fLncrCMKDKZTLpdL67mj3oKb4ql7DrsZKkGBTNI8Sg8wYySVSqHT6djuWLTi1aKj8NTUQwAWkM3n87h9+7aNJ51OWz78cDhEKBRCLpez3PROp2P1Bvfu3bPtGw8ODnBxcWE9eDheelvcm2E+n+P09BR/93d/hy+++AKJRMK2IO31euahsctpuVzGZDJBq9UKzNZh8FMrc/n8GPBOp9O2n/POzo6lF6vFr96QCnmXNweu6UJXQKvnp+tGqZyboJ/lGlSF5MagaCCoB6E0nxZG8ro0btQDIB1KhcIECtKak8kEzWbTgvn0CrVNCtc3f14ulxgMBtaCZDQaWcrycrlcS+32eD28UvgaUCgUcOfOHZyfn79CG/BnAGvBM35nWigtVeBVS1FBoe56DW6mkAaEFe5nlZPWz2azWRQKBWSz2bXN39XjUMGkApPn0c1otra2zLpl5g6tfnX7ST2xqIkdRc/Pz3F+fm7WYFD2E+eB86e7ebG/1HQ6XWscyA6gAExhkPJzOXa9Z70mCw618y0rl5mGynnlc9VAvvtM3GftWuT6Ofc53gQ3EBz0d/7PbS+iVcx6H0HnUeNGPRylltTYabVamE6nttOaesZKqWpmkz6T0WhkRZw37e/h8Xp4pfA1gErhxYsXZgUpJ07hTEuWGTjaU579j0Kh0Cu1BfpS0DJXC01/1s9pOiHPA9zcSpljIz9erVaRy+XQaDTMKtO2FKxfIB1FYcL72N/fx87OjlX/MsC6tbWF8XiMWq2GwWBgGUWkikqlEqrVKtLpNMLhsFE+x8fHFlSmUHCrlDWDqdPpoNvtrlmNpJhIZzQaDbNkeX0K8263+wodx3Nom4bZbIbhcIjT01NTpsViEclk0noIBcWKlOt3vQb1HFRx03rWe3GfqZ5bDRN3njgeFfb0arUZYxBPz3HxGq7i0zFTUWqQndlzl5eXppwV9EI1bdW9B1JO+XzeKEtStx5vDq8Uvgb0ej2cnp6i2+3a4mY7A3XVNbioSoGCi8FpCikFU/VokWqqqioGde9da84NOCpIGbCgiT+n02mrCB4MBhaMVmGiQkirjjc3NzGdTvHs2TOcnJzYce+++y5SqRTi8Thu376NnZ2dNSFGZbpYLKzlAekrt/UD54bC283UAdatclrdblaXS1fQS+D/eR1+V0XBZ8A4yHw+t93KUqnUmmANykriWPm3m+pKXM9Ts5T0865SUEWknoGbAsznyvHSQwiivILOpdY8sVhct27hOfV4nofGBK+tbTC43rVpZDKZtP3Lq9WqdXP9/PPPMZ/Prc22jy98NbxS+Bowm82sKlSFJfBqPyEKHm1NwZdBhblLF7huM19AddXd44DXb7jyOjBmQFpLlZvGPXhOeiqLxQLxeByZTAah0NV2m+wsSgHDDYWy2az138lms+j3+xiPx5YhNJvN0O/3rVOnZlu51JhSHKqMeZw7X0EUnf6dClyFGI/R4/mMeW4KQSYRqIB0LV0+l5uUtPvsgqAC+6agrwp+HqdKxPUgNTnA9TSCKM6g+9I50uemMRZdq6qIVSloi29NrOC8kebiviMsGvU00pvDK4WvAeRGNSCnDcCYLUMFAFx3UA2Hw0a30GXn/7S4jVAlQqHlZoeoi66KhC+XUhR8OcnR01thqiWFMTlgppAyDgJcFWtR+YVCIcvh73Q6OD4+xvn5ub3ki8UCDx8+tKD0J598gmw2a1tL1ut1PHv2zAQAqQelKtwAKZUrgFcse7cgTFNLx+OxUXkMUHI+eC7OhyYI8Bg3vkIqkHQSx8Fr3pRl5BYKula+qzjcLB9N/1RPyw2YB3H9biDZVSpqzevvCh0vBTpwrag1pVQpTq5Tzq/G44ArZcv6FMYPVLlwHI1GA+l02npZRSIRM0I8vhpeKXwNSKVSqNVq6PV6axQSsB78VKHAF0It7OVyueY1BFEAel5tRaAbvWhgFFjvQsprq2dBAcDxkI+ngOXOcMlkEr1eb03B8Hrj8RihUMj2GQiFQpbeqVXaaqUuFgscHx+j0Wjg2bNnuLi4sBdfFRVpGff+1aLlONTjUkqDv+uc8L7dACXHSYVAuMFcpVZ0HOFw2OIYTLV1vTe3+ta9jgr2oOOD6CdXuQQpUP1/0PoKClhrYoQ7d/p3rkW3QO8mykzH4q5XKtd+v7/W/4j3qcYJ24vE43Hs7e0hHo+j0+nYWvaFba+HVwpfA5LJJAqFggW7bgrq6e/K81JwKJdLBCkE/Tu/blIcxE1csI7TVVqj0QjD4dDaVGcyGVxeXprHQCuTCiwcvm4ax0Cu5uirx0KLkQVmq9VVHx6+7GptqlXIMQelK1Kw8DufhdJf+gzUY3KfGc8TFJANgs6rehMqnPlzUNxDEUQBKW6ia1xahvN303FKCar34HoHvKbeKxWA/s1d11on457D9XbcueV5mJGkayzonPTUKpUKFouFZc2ph+c9h2B4pfA1gHn4jx49QjQatT77hLZMAF4V6C49QXARq2WuLy6tbb74QVSDvghvogwUbKGcTqdRq9VQLpdxdnZmG82o608aa2dnB5PJBIPBwIrA3L5Lem1WBgPXWT1sLufei/uC838u7UFlxXnRYLBSUaTa2FqBVqjy8+rlaVCUAjXICnXnX+MNrsIiXK8gaI0wkSBIuKrQ03nWluWu0cDnF5TB5MZOlIbifbl1EW5ci38LSsVWuApIi+u4rnjfOn88N7PGWCvCbVS//PJLay3D7DmPV+GVwq8RqVQK/+t//S9sbGyg3+9boFR5VGBdWGuqHbAuNFR4AK/WM/B4V4ir5fg6Fz3o/0HH86Wbz6+2Qnz48CGOjo6Qz+ftHvv9/hotQ4ExHo/R6/XWAu8asOUcuBSEzkkQFRZ0HypIXWtclQaDvsqj65wz00tpLV6Lws3dmMg9B4BXnpsbo9DnpoJQ4yXKv+vc6ly5GVg6l+46UGHLe9C/u3Ooxwd5qIogg0O9B9cDC6LzdM3zPtQLcRU/1xmL4ebzuW2jypbpq9UKpVLJmuZ1Oh1Lsx0Oh6+9p7cRXin8mpDJZLC5uYk///M/R6/Xw8HBwdp+vaoIyC9T0KoVS6hg4Of17y7UGtSXTIWQ+1KrwNCYhUIFKl86egfJZNKyghhQpFCl4KWHwCIzjkOpGK3kdhWhq/TcNEaOXxWl6zmpoHYFqvs5DZDrHOl88DgKLrWC3e/8vyoQvcegehK3dbQKVo7npmB10LN1/x7kIepxOm/u+nM/5/7uUjJB59fnps/FPZ73rR6Iq/T5PPVcmUzGamZYaJlMJpHNZq1amokfTObwVNI1Qqs3nI2bLE6PK/yP//E/8J/+039Cq9VCs9nE5eUlXr58aYKDhU/xeNx2BHOzVVQAu3y4gi8MnwkXtabuuRvyuDQLhR//x7+r9ajCl/UITPfTz3FMrmLiy6qCnPeklMBNFqhrSXNMrjWs98hzqpBQ7t4VckGUhyoSbfLnjte1jF3+np/Tv1GZELyH6XRqsROllijcwuGwWb0cF+kjPpdEImFxKPZacu81Fou94n1qLYs7D+45dMz8LK11TTUl3PvV2JB7jEsP6rXcZxQOX9Us8P/dbhepVAr5fH6tVf3R0ZHFpRiofvz4sSV/bG9v4/LyEt1uF28D3kTce0/hV0Q2m8V//+//Hd/97nexsbEBAOj3+1gsFhZUZaCVnCcFlgpVDfbpg+PfXxcbANatYxcqXF26JEiQuefXOIEqEz2nfsa1Cnlu5eFVyCstowJdP+tayByXew3+PSgLSMd9U18g11u4KftGc9/5u6uk1OoN8vA0LdSNbVDw856j0SiKxSJisRiSySSKxaJtScmEBjbbY78oemSdTscaALKIkl6qG3TVMWvsR2mroGI6Xccu5abz5s5x0LXdY3V98bOaFBAOX22lysK1cDiM0WiETqdjSlLjB9xOlX2WvooWe9vglcKvgFgshkqlgv/23/4bCoWCNWJj+qYKP92sxX0pgFcDi0pPADdvlKJwYw3uca4r/7pzBgUh+XcVgDcpoqAxqQByvRRXeKhicM/nWqNBgsWlzfR6OqYgT0WvHRQEDbrHIAXv0lTu59WKdp8b24SEw2HbA2JrawuZTAa5XA7b29solUrY2dmxLSwTiYRt88lmfrPZDGdnZ3j27BmeP3+Oer0O4Ho98ppBlBJ/5ti1KR4/97rMqSCaj+eml0zjRJ9D0LzrWKiYVHFzb+1yuYxms2mxLBoz9MTC4TAKhYK1mKFSeJ3H+rbBK4VfAf/u3/07/NEf/REePHhgAn1zc9P2+x0MBrbPbKlUAgDLelDLX5UDgDXloMViwPW+yC49xM8R+kK61JQr2FwB6wpdgi/yTS9PkBB2r611GK4iUGGgSjNofO69BFEPrrWqMQ13jEFCiQpQYx1aZRukhIM8FjUCmD1D6JzwWXP/4kgkgmq1igcPHuDjjz/GBx98YE0B0+n0mnfA+9HGdRzXeDzGT3/6U/z4xz/G3/zN31iHV1eZcTwuXCXpejXuc3apOXd9633zOXHsLm2nnpR6Ceyau7GxgVqtZnVB9Xodjx49slYoiUTCrknlwy64oVAI7XYbuVwO5XIZx8fHPrYArxR+KUSjUWxsbGBnZweVSgWHh4fWMz+XywG4opXu3LmDSCSCfr+PwWCw5nq7qX9BtIwKFCJIQBJBrrb+j5ZZkFC/KRitcK3CIGvODaoqqFSUkw6CS9ncNKYgnptwrV/3c6/zcNygu6ssXjfHN92HPmtXeWocgwYB4wV3797Fu+++i3fffRfFYtGoolgstvZMNUuHgpN0XTabRS6XQy6XW7PyVUnqPajiDrpP99nzmlrcp//X8b0u2eF1z0C9Av6f27Lm83msVld1Lefn5xaz03Rd7QnFwku2bNcKeA+vFH4pJBIJ6/iZy+Xw85//HJeXl2i326hWq9jd3cWtW7ewv7+P+XyOo6MjdLtd27qRQl7bY6ulqsLXDXRScNBi1QCd8tAayFaaQi3cIKHoUloaa9D/KY1AqNXoCpQgYaT3p6BgCiq04jn0ehyr+389xr2+Zra4nwm6ltIevCfX49D7vWlM7pxzPHwuVAqh0NXmQg8ePMCDBw9w7969tfRUVSZqkfP/odB1gD+dTiORSCCZTL5CHSqFo+Pm2Jky7SoBjp3KS7N4qJR4b0GKM8izc+fEvS+CY2d8JZVKYTgcot1u4/T01NqVMDtO4yNci9zqMxwOW3NFjyt4pfALgi0s/s2/+TeIxWI4PDzE3//939tie/78OQ4ODrCzs4P79+/j9u3byOVy+OEPf4hms4mLiwtkMhnLk9b++hS0ysGq5agpitpThse7lExQK4SgwKkKatd1d616tfY0g+gm7j/o5XcVhWsRalGXC5fHDsoeYrdYAGt9pvRaQZ6ES3G5c0ZBy+sG3aM7lqCfeRzz6AFYi2qed39/H3fv3sWnn36KcrlsgVIKYPUKeE3dS5oeAnDVtffs7AzPnz/HYDCwbJygMXFc6rlqfy4tPHPnRtM8eR3+HgqFMB6P7dm6LTh0DIQ+Z2Y26WeazSYAoN1uIxQKrXnqjBfo+JSuSqVSpsjYCr5SqWA4HL71tQteKfyC2NzcxN27d7Gzs4Nut2s7qyUSCeul3+v1cH5+jlKphFwuh52dHbz33nu4vLzE0dGRudkqCG4SLDeBAikoG4f/59/cl+5Nzs/jggSHC9cadMfAY1xKQeEKaXo5QePS+XLPo0L9Jis/iKrT426y6nlPqkBfF6B83Xnca1F4RaNRbG5u4p133sH777+PSqViu+uR+uBxvH/Ov1KTPPdiscDLly9xdHSE09NTU+qu0nWFsSvwaTjQS9XWI/R0gnoa8X/8Wb08FdZfFeTVz6nCH41GVmXPvTeYfsrW6rxfFiXq/W1tba0ZOswU5D4dbyO8UvgF8f777+ODDz7AnTt38M///M84Ozsz6y0ej1tg7/z8HMlkEvfu3cN7772HSqWCy8tL/Mu//AuePXuG4XCIcDiMVCq15urTIuLC18Ia/e5a/Bqo4/9VOADBhW2vgx5zE1VDay6o+E5pINeidqkVCjANJgbdA5WgG2jmnGncxq30dWMqriJyqRUdgwpIWuJB86RwFaGb+cQ5WS6v96eORqN477338Ad/8Af4+OOPzdqnEcGfVfGRhqRRop7laDTCZ599hp///Od4/vz5GkWp98kxabNCBmlV8fD66qmsVlfNAilQ9b5UAekcKe3FrD0eyzl2FRcD/SrctfEi91ro9XoYDofodrtre5NorIXzdevWLbtuo9FAIpGwrWC9UvB4LdLpNG7fvo2PPvoIe3t7+OKLL2yPYO4i1u12rUAoFovh7OzM0lD/zb/5N/jWt76Fjz/+GJ999hmOj4/x6NEjyx13U1RVcOmLHFQgpHx/EH2kdM3reFziq6x+lxLieFTpBCkBjpNK0LVEqRjcFtcck/LUqvwikastM5Wf5/0r3eQqH2B9k5ubWlbrOW/q7+NmOrmKmXPgBqpVwBWLRWxubuKP//iPcefOHaTTaTSbzTXhSh6dKdD84n2RqlmtVnj27BmePXuGv/qrv8Lp6SmGw6Fx60rh6M9ch6SJlEIkVHDzeuHwVW0Av6sHdFPGlq7joCQMzaDjcfqecPOcWCyGfD6PXC5nged2u43ZbIZ2u/1KDUkqlcJ0OkUoFLK6hlKphL/+67+2/c/fVoUAeKXwxkgkEtje3kY6ncZyubSXjC+NFmbpYu71ejg+Pka9XkcsFsPW1hZu376NRCKB4XCIWCyGXq+39hJqiwHXUnZxk0LQ/wfhJi/BDTZq0Ni9ZhAd9FU0mKtMbhqv277hqygGnYObPAE99k2oOvVC3L+pInPvLyiOclPWkgrIRCJhFu98Pker1cLFxcXa8QzsjkYjpNNpo0t4DnqLjUYDBwcH+OKLL3B2dmaNBt214no8QffhjlX/pvUrhGYvuU3n3MC/em6u4REUc3JpN94PN4DiZlAs4nNrJYArb4z1CdFoFNVqFclk0tpuayfftxFeKbwhUqkU3nvvPYRCITSbTTx58gSRSASZTMYWPgUX883z+bzFHTKZDPb395FIJGyf4nw+j6dPn+L8/Bynp6fm9qorDaxXK7sKyH1B1dpVC4tCXoOjCp5D89y1gC6o+pUUgGZT8VwuheMKNiq/oJYSPJ6eAb2tcHi9rYKOO6iCVukazcaigKCA5ee+yssi9FgKTzeLTK+jP7seEzGdTpFMJpHP57FYXO0rMRgMUK/XbStUrrXBYGA1DJ988glyudxa9fFkMsFnn32Gv/3bv8Vnn31mFGcikbC9KFTZu8pW6TsF51ID4m6iRDweR6lUMk6fqdgE1y49JCZN6DoOojuD4kB8ntPpFO12G+PxGO12G6PRCP1+3/Zn1rUEwAL1XHu7u7uIxWL4y7/8S1xcXKDf73tPweOrEY/HLTtB+/wzg4iCii8mdyZjQPDly5fo9XpoNBqoVCprnTpLpRKSyaRlPlxcXGAwGFgBDkGrSIW1CiitZ1BLW61UDdQqDUMX2+XheV2exxUWGix0XzYNyLqBTFfBUPi798rPqzUcZG26sQr1OHh/bhrqTfPkzhGvy8+63sDrBIj7eU0O0HNQqPf7ffzrv/4rms0mWq0WACCXy5lHMBwO0Wg08N5771naJecgk8ng/PwcR0dH+Mu//Es8fvwYl5eXFhvQ+JQ+D+4PzrkLSjfWe9VWLVTUzPhKp9OoVCqIRCLW8pxWO4/lvAZ5ofos3OurARAKhdZiGDSoGCSeTCZGYwWtWT6D4XCIXC6HjY0N3Lp1C+PxGJ1O58bn+TbAK4U3AAtdkskkut3uWsoaF7jL14dCobWgH/shzWYzdLtdJBIJ2/JRKSdar8zuuClf/6bgZpCACnK/1W3X72pRucVqN1Eump7I8/B3DVC6wUb3XEG0C88fBJey0Hu4aW40hqFj5Xy4SuUm4ajH6FiC5saNAQUF7VUQdzodNJtNNJtNZDIZpFIpa3LHtE79u8Y8Tk9PcXBwgMPDQ+PVmQBx0zpyaaGg+abB49JN2mKDGUj0WPl/dyc7NWbcZx5EUek86dy5iopzwLgAPRD3uahipOGWz+dRKpWQSqVeufe3DV4pvAH29vawt7dncYBer7fWNlrdbQqnaDSKfr9v1hFf6sFggNPTUwDrKXpUBhQibjGTuwObG4h2YxCKm4Q4aS79ux6vL63SE8wAoaVGr4gKMJlMrrV2DgqMM6D8OiVxkxWp1npQMFl3WNM0VDfWoNa/jksVgrvrG5+PHnfTmOnVuZsKBe1/QE+Lc8rgea1WQy6XQzqdxtnZGeLxOO7evYsPP/wQmUwG9Xp9bb+Jv/u7v8Pjx4/x9OlTRCKRNYXgZhAFCX8G7JnZQ6FPj5ieDjexYZV1IpFAo9HAeDzG4eEh0um0UU2JRMKoJioNV1mqF+p6fq5H5hbHKc3KZ+CuKzV2GGQGrmo42Gxwd3cXBwcHbzV1BHil8EbgxvOZTMY4TODammEfFX3J3IwYt9+R8u6r1VUaq2tNUumoAOL5gjZocYV/ENWiv7tWn1IZvAe+gBRu3Pc2l8utWavD4RDHx8fodrtoNBp2Xm28FmTZu7ERHXNQfydSIDdZ5OoFuBksQcdwDPzuPscgxfUmCKIsXOXiBj851+oJfOtb37IMGyoXFk2ytUS320W/38fx8TFevHiBVqtlNI3bIdcNvFLx8YsCPhwOWyon4wPsuVSr1azdxmAwsLRP7h8ynU5tlz5SikwB1RiVzrV+dxWBmzzA80UiEaRSKaRSKSSTSRsnvXJgPVvMje1wvek13naFAHil8EZgiwC+LIQKMmA995pWIoWcvowUPvF4fE34a3yAC5RtBtzA5U2K4CYoRfS6z2gQVqmsUCiEdDqNQqGA999/H8ViEfl83l708XiMdDqNy8tL43jp7QTB5e0pDCgsbvJ6OM8cq0szEEEKj/yyOwdBVrPOdRBl4V7HpTpummP9uxbhubw+6cq9vT1LOWX3T1bhEmdnZ9YIjvEudw6Cxh4EBoGDdiaLx+NIpVLWQ4nGzHA4tH211WDRZABVisCrsRx+12eq70LQ/ajHks/nTRkx08rdtY7n4PldypNB8zd9p76p8ErhDRAOh80NLpVKGA6H5spHIhHb0Wk2m2E0GtkiTiaTa1YrrSgKGhX2KhBUQKlSCbK0eYz7Eul5NatHLacgnpa/K+VABVcsFvHOO+/gP/7H/4hcLodYLIZ2uw3gSmB8+OGHaDab+Pzzz/HDH/4Qp6en6Pf7di2Xd9diIoKKkha660koFUMqQ+MuHCuvwSwXtQQ5Fn53aw90bnQDGlUmzJrhuXkvbndPfs5VOvpMY7EYqtUqcrkckskkIpEIdnd3sbe3h7t372IwGKDZbGKxWFjn3Uwmg0wmg0qlgouLCxweHuL58+coFAqIx+O2iQzvISimwDFyvtlqmwoqn8+jXC4bJUhvoNVqWcKF7llA74HtLgBYnIHn0PVMStRd7/zdHS/nmD9nMhkUi0WUSiXcvn3bBHuxWLSNrobDIZbL5VofpFgshvF4jHA4jGKxaEHx4+Nj9Ho9rxR+2wP4fQAXbzwex8bGhrn4tO61YpcLnoudgoyCS/O2NUDtWqR6DM9NyybIqiVV4MYFCDfgply/WrZKL3FcPLbb7eLs7AxPnjzB1tYWarWaVaNOJhPjZqkcwuEwnjx5cqPVH3TPrpJ0hQjHpimKmlmkgeLXBTL1Hl1odkxQUJjHqJeiCuAmyzboPHy28/kc/X4fkUgEDx48wN27d3Hv3j2Ew2HLOCJNwtYMo9EIx8fHOD8/R7fbRSaTMWpGFVWQVc650AA754NbWA6HQ1QqFePb6/W6Veszowi4TkV1vTadGypwHuN63TzODfi/bv64tSYDy/F43N5Rthg/ODiw9eEmRIRCIeTzeUwmE5yfn1s9x+uu/TbAK4VfALFYDKVSCaFQyHhcxhN0K0wVEHwRXtdvx3Vv1UJSq1OFWNALxfO9LmOJ51ThpWNwhagqDubNP3v2zJQkqQTu8ZBKpaxL7Gg0spfyTeCmaFLJ6f273oYrnDWzJSiIqX9zz+XOJ6/tznVQZpJ6LzwmiFpy51stahYz5vN51Go1bG1t4ezsDOPxGP1+3/YezmazaDQamEwmODs7Q7PZxHA4tKCye183dcZ174v/o5cxHA6RyWSwWq2Qy+VsS9lms2lKh96yBvJdZUthr+8IvzTmpHMbBL5PBDOMmJVF7+n+/fuIRqNW76FrRD3scPgqhZap3+1223dLhVcKb4RUKoVsNotUKmVbIs7nc1xcXFh/FQoZtVBJnQBYs6Ruyq5wA6CEGxRVL0S9B1IlPBfhBnUJbcv9VQE2Wqzj8Rj/9//+X+zu7uK9997Dn/7pn6JcLqNWq6HdbmO5XCKVSuGdd95BNBrFo0ePrLAomUya10GB6woxVUIUGplMBsCVR9BsNk0YuFSNSzcxf57QmgV3bl0L9yYvwh2ffkav7VZg6/rgsYvFwjK1ZrOZzRETGjqdDo6PjzEcDlEoFHDv3j0AV5vRf/nll3jy5An++Z//eY06o0DPZDJr3pWrEGg4cBz0ZpkBRQ/j4uICvV4P4XAYjUbDYkaskWBTx0QisaaoWYSpc01v0jVa3ASMoBgA55A1QVwPBGlMGl9UkKQGVdjzZyqoZ8+e2bqaTqdr1NvbCK8U3gC0QMLhsAXWRqORudCamcMUVQBmrZD+CLJeg7IsbvIo1EJ1BY4rwIJaI/M6QX9zrSn3fBTCoVDICuyWyyUymQxu3bqFBw8erFU2ZzIZVKtV3Lp1y1JWlXNXKuimQK4bOwiHw2vFWvr9Jq+HVioFPWMIKlBuiicE/S9oflzlBqwHkfX6QTw51wm/ptMpBoMB+v2+ZXlxXuv1Op4/f46f//znOD8/X4tzaKdSKgQKRq065v91DBqM1/gV4wVc70rtMB6STCaRy+WMEgqHw2i32xgMBuh0OnZerX4OopmCaEx3PXB8LrWn673ZbFpiCO9tMpmYgcDUWFKfrVYL5+fnFkt52+Fn4A1ApcAUwMFggMFggOFwaDnPtEqV9gBgn9EWvqo0bkq5U2hePrBOnajXoG0ctHJUBWMQlaOKIkgh8Bh+zWYzNBoNtNttq7KtVCprlaypVMoCgPV63bwIVRwatFUBrlgul9YFlNapG1B3ERS4dBVDUBaSe90gZRs0Zn1mSnnxeBU06lHwPAxaU0ixTQOzariunj59isPDQ/zwhz+0LSeVmlTLOKiSm4pdn7mrXHVNaa3JZDJ5RZHSIs9kMtaQLh6PmwfRarXMguecclxKq/KZ3fQsdH1ybK5xo9X0nU7H4n1UUtpddTabWUB/Npuh2Wzi/Pw8sJL7bYRXCm+AnZ0d3LlzB9vb22i1WlZSrw24WM7Phc7gNIWGFqfR4tLtC+myu0JZBQprGbRhntvriOcgxeXSHdp+mdD2B67S0BdFg9m87uHhoVXZ/smf/In1/5/P58jn8/iDP/gDCwI+evTIxqCZTS6t5lJC3W7XMltUAXLMtGYpYDmHHKPGEzRwTvA8FCLqDQR5ZYQKNY7b3ROYz9ItstP5pafAHPuTkxPM53MMBgPUajVrA/2DH/wAR0dHFtMhmPGmmVLA9c5kpEt4Lc4XhaQqVw3y82fWJ3BtNRoN89qy2SyKxSK2trawu7uLfD5vbeLPzs7Q6/WsE3CQd0DjgNdXqlMNJSoRrf3RsQ8GgzXDjP9TqlG9KP786NEjPHz4EC9fvrRMpbcdXim8AbjHLfu7hEIhW2x0+d0MIfUCVOgQX5Vdwf8zR12tbBWkCgp/7YsErFv/Gsh1P3tThlBQ8JmYTqfodDo4ODjAnTt3sFpdNRijwOQmQ6PRCGdnZ5bWyPvkON0AbVBWEhUS75+CQudM55WKQrNaXAvTtdyBa2vUvVdFkGDivN70zDlOFYDk8EkbKTXHBm+9Xg/NZhOHh4dotVp2P+pduUkOei8aY1EPQcem1KFbtBiLxawtdS6Xs/FxXVGhsQJ6sVigUChgNpuhWCwa9UQL361oVmXvzq2OzTWA9DvHrkYM08RplPF+aYDNZjPL3OKGPB5eKbwRuOm5FqZxwVEpuAKfwpmLW4OBekzQQqRS0ZcOuA6AAuv0Bb+UPtICIvfl59+pZFzPxs3Q0eCpUhP8bLfbxXg8xs7ODpbLJba3t+0lZswhGo3i5cuXVrvAwCStZM6RtqtwqRry5BQS7Nbp8vcs+FO43ocLzp96TG7A2eWxlX+/KRuMgtBVyrwvKoXl8irFlJXh/X7fNthhLyQKY6070IwevS6w3qFWvYQgr1D7bam1zSBysVi0vkBsG8+tTplO22g0sFgsrCleqVRCuVy27Ch91oR6nRy/+3x0bPps9L54Lq0KpxdPpcDzMi41mUzw8uVLdLtd7yEIvFJ4A7DlAN37Xq9naXC07ig0dYMOFRgUDuTHaflqewG+gLppimtx6udUSfAYFewaV1Dhxs+wXYfGIQCYslP6JEg5AFh7CX/+85+j0+kgk8ng3r17yGazmM/nqNVqyOfzSKVSePjwIZ4+fYoXL15YD3xeh0Ke1BppHpfO0TRO/q60DdtDK83mCsCblEMQr/1VKbU3Wa28F5e/57OgtcrfKciePXtm20tq8JdCXVuf8H9ubYxa24lE4pW4BjOedFxKR65WV5XNm5ub2NrawjvvvLNGDV5cXODy8tKMhHq9jrOzM6TTaRwfH1umlGtoUHGrctd1xaC4fkYpUN2alHQPkzu4RpSe1QSPbDZrVdmHh4eo1+uo1+trAXAPrxTeCKlUyjZZZ3trLiQuSnVb9SVzEZTpQguHG31QWCg9pRW+ah0plIJx/6bX5gvmejd8QTWXW8/jWsPMKOF4er0e6vU6jo6OUKvVLPtjubzaSnJrawvT6dSEXbfbtRbhasm79IHGNFRAsE8Pu9gy3jIYDKwPDhWEWuu8d6Vbbgp0Bs2p+xxdxRxE7SnU2wPWPSQAJuxumnP1HF36K2hcLv3Cc6hi1/uicNXWG6PRyBTT7u6uWe69Xm+tK+l0OkWz2bSd4ZLJJFKpFEaj0SsZZ1z7mqjB+XOLPNUwUc/Bpf74WX1v6P2Q/mW/pm63e2MW3NsMrxTeANlsFslk0sr6W62WCTJaJMB1OwyXbwau6Qu1sinYtBd9Op22l4vcu1qHoVDIgq5qZfE45W0JvjRKB6ll6WZd8He375Jy4bw2C64ymYz1nHn48CF2dnaQTCZRLpftPBsbGygWi3j33XdRKpVwcnKCly9f4uzszHrWqBdFuEFijiGfz2NzcxMbGxsoFAqYTCZW/aubrCgHr96VBrqD5kfhCmfXa3GFk/vs+RkKMno3pKT4rKPRKDqdjlnM9Gr0mbGlhHoRXBe8rusVuokO/F+QAqNCYFB3PB7jxYsXds/7+/soFAooFot4/PixrYdUKmVbYJ6fnyObzaJQKKBcLmO5XOLs7GxtnnW9UXlwrWv8SGNzzBziXsq8NgvQ6L1znqgUuH/zbDbDyckJzs7OrL6ISvFt33GN8ErhK6Av8sXFhe3spBwl2wsAr1rmXNhBQiaVStlLTQ8BwCuBL74YqVTK6Bo3YMhrBGXLqFWmQoz35hZ4Ea43wnukd6DKabG42h94Npvh+PgYn332GXq9Hr7//e/b3hHj8RixWAzFYhHf+9730Ov10G630Wg00Ol00Gg0cHx8bBy6xi1oHWazWZRKJRSLRTx48AC1Wg3lctmuPZlM0Gq1rPL64cOH6PV6Rs3ohi+uQA9Seq53ospBA97ETV6O/p9CvVQqIZfLAbji6cn3v/POO9jZ2bGCQPbxUUs5yAMMij1xfPxivYgmLVD5ADA6a7G4anJ4enqK8/NzhMNXLUtqtRq+/e1vo1wu45133rGqau4Gt1qtbFtLrnE2rXP30eY9aMqx1lco5cT7o7fO94GtP3geGlM8f6FQQDabRa1Ww/HxMRqNBp49e4bRaGTnfp1X9zbCK4XXIJlMolKpWGEOcLUwdT9l1/q8CRTOtAA1QykSiaBcLq8tblfAa7aNm0VCfhZY36OBliD5VqUPXIpJeXG9tlJh+vJorILzwnsbjUaW972/v2/3puMoFApW9JTP5y39MpvNmoKg8Obn4vG4WZ6lUgl37txBPp9HNps1i3s6nVrB13K5NJpgtVqh1WphMBhY7rwqbZfvD0IQnePOTdBnXTouHA7b1pvlchkATIjOZjNsbm5iZ2fHdvoDXm3hHUQXKTS7KOiegmhMCkj1PNUwWSwWGA6HKBaLWC6XZt2zKptrmet4Op2i1+sZx6+ZYJocoLRh0H3pe6ZxOb6HrI2g0RGLxdaSEUgpdbtdC3rznj1ehVcKr8HOzg7+/b//97h//z5KpRLOzs5MCFM5aBsBl6N2M3lUqLvW73e/+12rAD04OABwlfnB/G4t9lEKSrN1CA0Y8rrkbPXlo8XGTeB5HAWAWuoalKTioIej98l7Ozo6Mhrk448/xp07d2zvXqYnsqVDoVCwc9P91574qhjS6bRZn8C14CKFooqmUCggl8uZZfn48WPLflKBqYHrN+GV3VoSpYlUAajnQW9sPp8jm82iUqlgf38ftVoNkchVB1o2nLt79y6KxSISiQR+/OMfo9PpmMALoqk0K4xQz5SepQp4rgOuDwb5NetNA7ikT5vNJjqdDu7fv48PP/wQhULB2sBwD4VcLod+v4/xeGz3REHO90ING15TDSBSYXy2BAPk8/kc7XbbCua0M2sqlbJ1tFwurWfXkydPzEAg/eYpo1fhlcJrwMV5cXFh1jMzOUajkb1MavnrLliaI64CjguS/ChbQoxGI4tVqMVG64ecMIW0tjtWvp0CV68HvJp/T/pKOXdSG9zkndY/LXHSROopcEzqNTDA+9lnn2E4HOLo6Ah/+Id/aPEZ7S+j88U5pzXK58Dz8xoUMsB664bZbGYVrfP5HLdu3TKK7ujoyILjFIKaxaOUi86LG7txLVoKUSpvDYy688/YCntHpVIphEIhZLNZpNNpC34yK+b4+Ni2jORY3XYWLjhWCstcLmfUi0sd8R64ljQrCLhWOKSYmFDw5Zdf4uLiAtvb2wCwFqdQD48GBKk7V6HqfGsnACoO9XAJvmv0Pkl19ft9i2vxmH6/j9FoZFuT8rnxPoOU7NsOrxReA2ayPHr0COVy2RYs+Wu1cIJc0a/KQqJLrbtqaeMuChzy93Txafkp3+0WKWmcgC9VJBKxYB5jFrwGcM2RM/A9mUzWeGwN2n7VvPG+m82meTx7e3sWgA6iDSiYXU6fY9f7dlsu8HqkryiEstnsWpoj/65z6QaElc8OopeCnmXQnOi98FrRaBSVSgWbm5uoVCprMR/GarhXAfcD5zrh/bnrSSlM10OIRqNrCp73ogpWqSY+D64LPR87oq5WK/R6PfPmmCyhMSZ+cW1qRbfOiQutdnfjZrpW+VmmiY/HY2vFwa1AeVwkEsFkMrG1wM4A+g55xXANrxReg0ajgX/4h3/AX/3VX6FareJP//RPLdOGm5FoQRuw/oKyKyitb02/A655+EgkYv1u+v3+Gk1EL4Gfd4N00Wh0TcAnk0n7jL7o3DHr3r176Ha7eP78Oc7Ozl75HDd6WSwWOD09NdqoUqnY/dEjUc+EXpSOi14FA3zpdBrf/va3jQKil8PPU5G51Iy7K50qES264/FsC1Gv15FOpy018vLyEpeXl0bHcNMkfo4BSgpCpYI4Rje2ogFSXptjoYewWl21ni4UCtje3sZHH32Ezc1N1Go149zj8Ti2t7dRrVZxdHSEdruNer1uypjeF9eEKimNGfA7lQF5f65RBoSVbiHtpRa7eo/MQGJ9DKuWmcnDv5XLZaOSNAbBQk9VGNo1mDQVPWa2sWb7DvU61JjodDqWQcRsqXQ6jbt371r19XQ6xWQysTXfaDRwcnJiyRzcqc5XM1/DK4XXgP1n+OIxoDafz62mgBua68upQiqIY1arcTweo9PpGPcKBG8Qow3OGGxTl1trHPjFLR1LpRI2NjawtbWFbDaLk5MTtFotFItFs6z7/T6m0+laGqd2xdRKWHo1bnsCYL1SW4OXk8kET58+tRjEgwcPkEqlLAVXrX83QOrSUxRwFGZa9DSbzSxVUamvWCyGra0tDAYDy/SZz+fo9Xp2bvXYCLWW9Xm4lAZ/V0qOc5dIJLC9vW2xjtPTU7TbbbRaLRQKBesuymfIdcEmgjoWGgz696AA9Hx+1ZmW/DnPr4VhbgBcKTMNBHP+9P8U8MrJu3EuxoC4HrjnsxpJ9G5cq92txXAVnxpjpLZ4juFwaO8X7zUWi6FWq9lubexC+/z5cxtTKBRao6DeVnil8BpQ+FYqFZTLZVQqFavCJXVEgeK6xBQI+vJxsavCYKC32+2a1awvh1pvKmz4MvA6pJiAK2GqnStrtRr29vZw69YtC+Imk0lUq1UsFletg1kbQVdchb6+uMRXUUjA+gb18/kcl5eXePHiBSKRiO3olc/nX5lzVyG6VIP7O++f16QgCoVCtl0kPQO2LFGvQJW0jp3n5u+ul8D/q9DSanQaD5lMBqVSyeI8p6enAK5aPL///vuWnaXPj6mXOi8qDG+CelM0HIbDoVXKk/p0KTH+ruuVa4tKgOtN6SGtSOd1NTZA3l93auMz5DX4nYaItinRa+k86O/6LpDyBV5trsci1EwmYzVH9HSUIvVKweNGMEj4n//zf8ann36KP/3TP8U//MM/4OHDh7YAeQwXJXlXzQl3X2Kt1qQw7vV6WK1W5poDCPQ43CC2ZhW1Wi1TBH/8x3+MUqlk+0eXSiWkUimcnp6i2WxiOp1ia2sLwNWL1263LShHWqtara5VVVPYKY1FoU+h4abSaoByPp/jiy++wMHBAZ4/f469vT3cvn0b77//PrLZLDKZjL2gmt1FCkADy1TGpJfUo2Ob89lshsvLS1xcXFg21GQywd27d+1cuhnMYDBYo+YYlFbqSIVpEOWiAqVcLmNrawu3b99GtVrF5eUlfvzjH+Pg4MCSDLLZLPL5PEqlknHfg8HAMna0HQWVlqYgB60vjp2KaTAYGIWme2BTYIdCobW9B/T+udY0eKwUDr0rPjcaTfy7Zn8BQKlUshgEez3ps2D9gCokDaqrstZWL4wVsfYlmUyiUCisKXyOKZVKGZV4cHCA3d1d1Go1hEJXRXBsOvi2wiuF14B0Sr1eR6/XQyaTwc7Oju0fwEAs3XIWTwGvbvBCULgAWPMuxuPxGlXCRexW9upnGZildVer1XD37l3s7Ozg/v37JiyfPHlivG+r1UKz2US9XsfPf/5zrFYrNBoNK/DK5XKvWGMu9OUk1ELUuIrrOfFn7uHM83CryUwmg1gsZoV6tL5VGOk5+bsG1s/OznBxcYEnT57g8ePH6HQ66HQ61sCNngqLmrQ9A/lv1opoxoreGwWhq6i4Q186nbbitHw+bwJyb2/PsnH0Pvk82VyO7ciVklHh6MayuCY0EO5azkr7ALDAMNNI+bP2juJ9am8pzjvXHSlTjosZeuHw1aZU7BXGZ02PhdST0li6rtSwcJ+5/o1KW1NqGbPgZ0iFxmIx3Lp1C+l0GuVyGRcXF7h37x52dnZMsbCHk+utvS3wSuE1mM/ntln95eUlVquVWX/lctniAOo6a2oe/6bgC6QxCGb6qLWmVhJfBD2nmwUTDodRLpdx9+5d7O/vY2trCycnJ+j1enj69KkJRAZUaTUvFgv0+30TquzM6UIFeFBtBAWCBpp1/EpHATDLXjl/5utToLI1AXe90/tXr0UF52w2w8uXL3FwcIB/+Zd/wcuXL9cEEj071gFQKVAIM4uFNIZeD8Da/anHwuD59vY2SqUSSqWSWapcF/l8Hrdu3bI4jsZrOL7lcml9gqjAdK51zl06z6XcOFa9H7Wuud54DVJMNHZ4n6xT0KCzZmdx7t0Gj5xPbkilY9W1r16BetG8R1WIuv6UouU1GavQlGP9TDgcxtbWFgqFAgqFAp4+fYp79+5hd3cXw+EQL1++RCwWs7oLd3OhtwFeKbwB/vEf/xH9fh/37t1DOp3GeDxGtVq1ApnDw0MUi0UrqQeuFmy/31/j/4HrtFAuXC46cuF8UZmVwp23KLT5YqrVzZ5J5MpPT09xcnKCg4MDHB0doV6vv+JxMOALYK2ZHHPgQ6HQ2kuu/WTUkwhSTsyOoQCmoNJiP2YFhUIh632k++yyCC2TyVj8gT+nUimjzDgn3D3rxYsX+OKLL9But9HtdgFcW6aMI5BuYHCd88h5YfPD+XyOdDptHDfpEZfGqFar2NzcRLVaxXvvvYd8Po9MJmOWZq/Xs53o9vb2rJfQo0ePMJvN8Pz5c/zJn/wJNjc3LbtNqSKdbzfLSEGDA7jOPqIi1ngUBS03hQqHw+h0OlbTsL29jWKxiO3tbWSzWbPqO52OeZT63LkmNTWUSoSZXhTcpMYSiYRln2mwmjScGwh3O7oC1+mroVDIKqsTicRaS28dX6FQsLXEWEg+n0e/38eLFy8sg2l7exv1eh2JRAJ3797F4eHhK8rqmwyvFN4AvV4Px8fH+Pu//3sTcvP5HHt7e9ja2sLDhw8BwJq6EVrtzO9qHVHoxuNxVCoVVKtVJJNJ7O7uotVq2Y5jas1RQDDziRkekUgE/X4fp6en6HQ6SKfT6HQ6lnLHz/KFoyUZlLkSZBlz/IwNuAFW4LpHEbNp1L0HYPUPtODI5wPXrT14TWarsDdSPp83eonX1awVCt9Wq2X585qdwnsPh8MWa3GzcUgfUFHz3NqqWo/nF9OIB4OBCUQWNfJ5bWxsmNAaj8fWev3o6MiqmOkdUXCl02nj2FUIuxlS7vPTZ0glzfiTZulosgQVJ9uIsNU5PQNm5dCjctcLvzN1lr9TyPNn9SZvWn9uUDnof0qbMo5ExaAKRMeay+WQSqXQ6XTw4sULnJycrFXQM3W3XC6j1WrZO8zCUt1a9JsMrxTeAKPRCCcnJ/ibv/kbs8y/853v4IMPPkA+n7emZRTCwLXFrIVkau1xUadSKcvPrlarJgjS6bRZrJqFxJeAOeFs+TCbzYw/Z8472xoTFJKatqgvm5sRoj9TmAOvZsJohgqVAo9VD8XduU4DxDwPPQD+j9QK23CQIqKgDsovpyeWSCTsHFRkiUQCm5ubyOfziMfjFuCn8OP+21QgpHdUCeic8X/coP758+drApmZR+Fw2DKf+v2+1TOcn59jOBzi/Pwc9+/ft/FVKhXk83mLNRFKo6mnxN/5s3LujB0A18FqJjVwfXIdsfspFRuDv41Gw+ItasG764dUmSoKTcrQz/F//F3TUIOga1ENLdbXUCnwOfG5c72yhxlbXhwfH6+1wGcPrUqlghcvXpiC29jYsAD22wCvFN4Q4/F4jW98+fIlotEoyuUyarUatra2rAUA21XQPY/H45YxEovF1gqqWHjGrRaz2Syq1aoJFLY50P2cAdg2jTw/LV3y8GqhKbS6ly+3cvWaekrqQS07/l2VnQaE9X9UHNqOg94Eq0vD4bC58rRaNVWUQcNKpYJkMonhcGjzkUwmzbrXmIneN6tbSUnR2xgMBmi1WlYQFg6H0ev11tpn8Lsb5OTfeD2OH7hOww2FQtaxs9FoWPCyVCpZsJPpuJlMBru7u9ZHqFKpoFAoWBYV51i5fo5FrWkdK8ceiUSwt7dnApNB9dFotKZcqPxo3FDIawacenka1+E8aJ0M1wQ9S1XMeg7l693fXSWiY+Xf3LgEFSAFOluEMIFhOBzi7OzM2oiwEJXj49otFovo9Xro9Xpv3VadXim8IRigJfVyenqK8XiMTCaDeDxuFZQAzCJVukT75ZMyYqES3VfSD6QiyNEqfQHArFpyxXTXtUKYFZsA1l5C9574XQPbrvVHgaFKhGl+PL8qAlIUSg9Q4Kuw4b24VqTGYfidVBPvKZlMolarrWWbaBom54IvPAP5zFNnzIDHALBMo5sscaWsdOyu56C1BJpuyevRy9vf37cMpZ2dHfNeWGBVqVRwenq6Jph1XJq04AaaCd6Ppi4HxYH0c66g5nm/SjAqRadGB9cHP++mLet1Xgf9jHq26rm6fbCoiEihTadT1Ot1U7DqdWrBXjKZtGQAl+b6psMrhTeEusXz+Xwto4cVw/xiWwrSGKRLVAjTEibVEo/Hba/jTqdj3ob2v+dL4Gar6EtOD0VrCyiklS6ie03w/BoYdnsj8Vy6mQvHxntWCoEvE4+hR0MFq3nxVD4ab6GA18wZANYu+9atW6hUKqhUKtjd3TV6jNYdeeBer4fz8/M1j4a0EYUCA57MoWc1sJvhQ6i1HCQs1WrlfJGbB6468H7wwQf44IMPUKlUsFqtrG4gn89b07ynT59iNput0UikEYOsZg1KU0hrKxAVhKqk3RiQngO43qtAs8gY23CFs1r8fMbMCnL3s9AMoZtoI/2fFoy6yo3e4GAwwHQ6te+8JxaKnp+f25xoMghjYUw4oBJ927br9ErhDUHrnwsoHo8jnU5jtVqZEKIFwpS3jY0NrFYrjEYjy9pgF08K2Eqlgkwmg1qtZil85C65ONW6puWn9IGCx+h+x7SElVKgVaS54/RiALwi7NwGexyTBj01k0fHpkVtWshGoaXCiZ9z892B6xhIs9lEu93G5eUlcrkcisUiPvzwQ2xvb1shEmkCzlc2mzUl0+12rc+U9irivbsKTaFxIfWG1NNS4aacNueVcQQ+ByYsqCV9+/Ztiyk8evQIjx8/tuce1FrC/Vl/Z7B6sVhY4gIFnhamuc+Az5Jrh+sxlUrZ/4LoRR6r1jvnis9fO/u6c0xQ4ahXwDnUuMF0OsXFxYXRde12237mfDGIzIwzGin8mfOhmU8aU/LZRx6vgALUXaCRSASlUgn5fB7FYhF7e3sol8soFotGGzFrg8JGUxyZpVIsFtcCwEoJUKgojcDPu+40f1d6hv+jItDPuFlEery2udDgolr1fHnclFdem2Pl54Lm1aUCXHBcFEAqDNjULB6P2wu/u7trc5tKpSxDiPtr0wPT+eQ4dF51rnRsbpCU96gCUuMRrvJWi/Xw8BCLxcJqJug5ctzlchnZbHbNi3LnyV2TfP5uxTUFIACjLd1n5EL/TjpUPUWt0+F9K8dPY4OCNag4jffgzpuuM96zrmt9BmxlD8D26KY3xGurB0vqVWsglAZ0jZFQKIRMJmOU0jcZXim8IZbLq6IiUkJcUIlEAt/+9rfx0Ucf4d1330WhUDAO+/DwEM1mEycnJ+h0OrYYdbFdXFxYIJpWKxd7KBRaa6XtxgWUHuALyLGqYtFCK6WWgFezWfiSUDAFUQpK9/D/6j3oSxz0ArnCQK+h2Sp8gVXAUYnyRWca6unpKYrFIjY3N/H9738ftVoNm5ubSCQS6HQ6OD09te6jGgtwBa3SQkHeAi1/VRzuvapS1eI9nZ/FYoHj42NMJhMcHBwgFLrKmafHuFpdNbIjVcY9KFyrnB7nTXNMxU1+Xa13bemhn1MjguC9MFGCc8FYmRoIXGcUpNls1mJfLAhzA8S6HoB1msilmPQdoKFDb73f768ZD2rxMz5HRUEvws0QpBehTf+YKv7ixYu1jL5vIrxS+AVBF3xjYwPf+973cP/+fezv72M0GqHZbOKzzz6zeECr1bJiHrW6deEzeNpqtcwTUeHIF16FgAofWm08H6/hvqT8rr10aB27QkXpHnX/9TM6JsJ19/mCaXoqBakqg6CaAgb7gHU6gnEAeiZMgaU1fHZ2hh/+8IcolUrY3t62HjsvXrywqlrNBlPOnde6yRJUS9I9Nkh5uhY0z0EBG41G0W63MR6P8fd///cIh8PWTqXRaKBer+Pk5ATz+Ry1Ws3Wkwol3ocGd5XXdzl/ttcgvahKWI/XwDLHrrEh/burQOgJJxIJ3LlzB3fv3rV014ODA1xcXKzFbLS/FOfGNToUum64trmGmW3E+6XiajabNndaP6QtxPkZN7uKCuMmmuubBq8UfkFQYGUyGezv7+ODDz5AKpXCwcEBzs/PLSuJVhFfNJcOAq4tY8Yd+GKpNeT+DOAVy4nncoNwKnxUSCm/D2CtoInWc9B9E27AlddXes0VjK+jiFTRKL1A4aA8fRCdxs+QIjk/P7eAOxUAdzRzqRbXU3Lv93XBT37+q+gEvT8X9BBPT0/x/PlzhEJXefJs2HZ5eYnRaGSWq9IanCMVyIQqCp03WvAcj3ou7r0HrQU3RqAGjB5DjyKXyyGbzdpeB6wY19oPpTB5f67HqV6F+5zcZ+rSeqTr2O7DfV5BlGXQ/1533DcJXin8guBiTaVSqNVqqFar+NnPfoYf//jHePz4sRVZsdKY1opmA3FxM+gHYC0bhy8FBZ5LSwDXwnM2m1lAViuJNU00k8nYdRnkBq4FIhvv0YpXSw24tvCDaAXg+sXTwDLHzNRcV4gHUUjueV26igqS80tKgnQSz9vtdi3jSBUjs6NUOJDzBmD1DrwHV9i4MQX3f6rwgzwwzjfXApXUYnG1odEPfvADPHz4ENvb22i329aqg+nL7twA17Qdwefg1okQ+gx0T4Ob5l+VENtAsA4gFArZrm7k2pmeXa1WUSwWkUql0Gg0MJvNcHZ2hul0ap4dnwO9FqVAde5IhbrxGq47/o3ncMHnylRlfRe5nnhtNWZ0/akX+02HVwq/ICgw2+02jo6OkEql8PjxY8sYottNF185ceX4KeDUYgNetSpd6oQLlC8NX2aljNR6XC7Xt/ik1aW7d1ERuFaYei78TiHmjtNVXKrYtLiL988gp1p6es6baBi1SpWzZ+CYwl4Vo0uH6HN0z0m4ln1QkFk9GTdgq3EhpdvUM9HzArAuu2q1d7vdtYwtFlgp3NRjvYcgb4HCVNeIeon8neNwEw6YqcPYGQVurVaz/Tv4XJ4/f24p1oyrqTAPmjumN6vw1mw1rg/1JGi4uPPLtae0kAp3vgvhcNjSnrmeGH9jlbTuRPhNhlcKvwBo3bMyllwlXXx9+bWOwLUiXagwDDpGX25Xiahw43F8eZUvdoWcHsPPuse4ykC9FL54NykOfTHdF1nvxxW2aq3q/4LmzOWcqRA0tVMVsOshuXP+VbGEm/7v0jE31ZDcRAsSFLIqDJlFo4o2KJah19Axu3Ot/39d7n0Q3cU1w3vTwq9oNIp8Pm/NAEnXdLtdNJtN8yTUmqdCVSpUPTvNNNLvrjdA4yNo7C4t5j5Djbex8JOKgXMWjUZtn4u3AV4pvCEikate+bu7u9jZ2cG//bf/1noO9Xo9ALBKWc3c4ctDkDIAri1dfXF5vOu+avsFpQQ0W0jdag0c0irU1DtmXehnKGhplWlbAo6d5yQdo20tNDinFroqE77wqVTK2hC4qbK8livY1PLnfei4SccxfsDAYjQatfx6t3WHBmQVanVrIZbGUFwaSYWWej+qRJU+1L+tViuLQbXbbesz5KYhq6XOa2hPKPVcSFFpYNitT9HMG8Zk6EXqPsqc98XievvWbrdrNQAs4AyFQmg0Gjg+PsZwOLR1Folc7cGsglbXKWmnTCYDAKYc3Wp+NRpU4Otccr5J1QXFitSY4v/T6TRqtRoqlQr6/f5a+q5vc+HxCrLZLL71rW/h7t27KJfLAIBGo2EvBnAlvLiIXQtRA85KrajQdIWJa92qBcSfmUnCF4/g9Si0lHpyLS51v/UlUouOx/EcPA8VjioP5dMZW2EqKc+VSCSsg6vO301cuSocVTYagOTv9OKUZuFYXeg9u/PCeaAycD02NxitFquuAVchuJYtjyHVd3l5+UqGFXn3VCqFfD5vNTAXFxcmNINqSfisdF24c8o5c4OqVEI6V/P53HbooxLiF1Nm2S6bKdsEd4DjfDKtmM3sNHVV27vcBPWAguYzyIui0lQjiu1PCoWC7Rne7/dtB7lms/lW0EaEVwpviGQyif39fdy5cweZTAa9Xs+ah2m/G7U8KZBpFbluMAWECg/X2idcl1lpIlrrN1EDrlIIuhY/o/EHrVYOcskp+PhdlRu/otGrHcf4stEyTKVS9vIFKSF9aV1lFKQg1ULmnFD4qIJ1FYOrEPReVQHc9LP7fG6iCTlupZRcQcZnTu6d52ERXiqVQqFQQK1WQzKZtK68/X7fhPvr6KmgMfGLwd+gzrzq6VGQs0mcPit6N1T2q9V1caZ6uxpsB2CdglOplHW11e6lnFd9Nvo3/uyuf3c96vPlu0OPKJlMolgs2jswHA4txtDr9W6kDr+J8ErhDUH3l5vPP3782NoNZzIZE3gawNXPEjctVBXkKryD+GzSN5FIxPrlANdtJoIyMHgu/o89fshbMxOFL68KJf5MC4v7N6h3osJ1uVxasLBYLGJ/fx/lchnD4RCNRgPtdhuhUMg6zvLeXSv2JgqJP3OcmkuutInu66xKmhavBsxV0bhZJipsXEHrPh9Vzhy/0nhEOBy2/bj5DHgtVXyr1QrFYhG1Wg337t2zjWLu3LmDwWCAn/3sZ/jBD35gbR7ctRS0BvS+qJjVsNE548+k05huTc+yWCxaqxE2dWRwnEVsGkDmGptOp0in09biZTqdotFoBFrlaphwzXI+1dvhvCrUMw8KWiu1lkgkcH5+juPjYxweHgLAWi+wtwVeKbwBaIGypfB8Prd863A4vFZ5exP3qMrAhVplNwkdHsOXVnsVuRa1m8ZKGkBpC7eojGMjL08lEER1UREpDUAPIxqNWj56MplEPp83T0H7zPB6rtXvxlh4X6owOT6ljFwrWYusOLe0bPV8yiu7VIv7DFQ5BHkOwKv0DO9Jj9H/69/1mamnQ3qnUqlYNXEodFUrc//+fVxcXCCVSlmNjN6nC9fSVgWm3gyhdBczn7QTaSqVwtbWFt577z2cn5/j7OwMp6enawpc40w8H42GRCJhbUcGg0EgveXSg2qA6HN0aTmuCV1Hes/8u2ZRqUHAQPrb5CUAXim8ESiE2u22WTu5XM4W3Wg0WrNQdfHxxXY3EidcFzgIyu/yhaJSUI9D3XI3f5s/KxiwZOA6FAoZjaDUhVIAq9VqrW8+uWNmX1EZkO7gDl608Dgeuve8J86N/q5CQX+mp+TGHfR50ZMJEtYu7aNBefcYFSJB19FjgxSL3o8qbx7veosuP67PoVgsIhaLGdeeTCaxt7eHe/fu2V4BANaqhVWJEu75KUxdAcrxKR1DiokJCWxhzg2nND7lKmt9DtxEiuMeDofWQdY91p1jl4Z1v+ux7vWBV/dpYF8k0l+cE413vU3wSuENwBeb5fh8Ofhyui+9Bl6VZnGFh3Lsutg1BsDrUQBqyh69FGC9YpXuuVqoyvtTELIdM7l/jsUNLOsmJDyGGT0UvEzN5T7TLOIbDoc4OjpCKBTC+fn5WiyEGw0xMMxrqBIgNH6ic6RzymwVbpZDy1atYX6W57ypkZ/SSgrNdQ9aI0pvuIYBz+1SNHptYL1jLeeZnyN9NBwO0Ww2cXFxgVAohM3NTUSjUbx8+dK2cqXlq20fNF7gxolcGo3Q9RcKhVCtVu0Y3XVwb28Pq9UKP//5z9FsNjGfz63YzY1NhUIhqwXgTnc6925MgNAAvBtHcsfM7/rOuvEjvmPz+RwvXrywKnLd0Ohtg1cKbwAKcZdyAF4VJBTEKrwUQYvVdYkJtUQZECPFw/Mr7+5+Vl98/VnpKvc+gqxcXocKgYKFHlMul8PGxoZVqyq1xiwVAKaoWNjE+dQMFQoDtUxdysOtXHWDvjq3r3uxeYz7jNRiVoqL43YtfJ17l/7gPQSl3fL8tOZ1/ErXUIG3Wi3E43Hkcjl0Oh20Wi28fPnS0p/p1TEwzefG1GL10nTO3Dngz0oTqXKJRq92zqMHyAwdCv9KpWIZRHw+biyDtAzvNYgO0uerc+WOUz1Llz5y3zeew/UiQqHQ2h4m3LTpbUpFJbxSeAOoBcuFpXvV0qrQALD78gVZYW4pv3oNrqCiZa3pia7LT7gKR2kmNxvHPV5pFO26SiHuZlYlEgmkUinkcjk7D9sbhMNXDd5chaNeUDh81TKEgn44HK69vDfFWHi8ZkLxXrQ3j/L1en3OL2m9IC6d3932By7UuuX/XYWklrar6NzP8FyLxcKKqgaDAc7OzhAOX23TenFxgcvLSxwcHGBzc3NtDhnAZ9pqu922NaDGhI5NrWmOievBnUsaA1tbW1ZXwL0rptMparUaBoMBQqEQhsPhK14fvWxVCrxn9a5cy57/1/Whcxf0HPT/GkPTIjw+l8lkYrGFVCqF1Wr1je+IGgSvFN4QzNLQojR90Sg4WHSzWq1eqSHg4lNXmpY+rRblZGlhujSUWto3pe1xnOqC60vvWt8q+LXPPq9ZKBRs/wL20E8mk0YVZTIZlMtlhMNhVKtVaw2QSqXsfGyEphkvKgAZC3GtM+1kqUFAYJ2K4+5gzJcPEvI8B5UIBYzGZrRXUxCNRaiQchWGCi/l9PU7hb5asQoaH/QkWCn885//HI8fP8ZyuUSpVMKtW7eQTqft2Eqlgmq1ajTij370I6N4eD3u18y1rWvZ9YY4F6RZ2OW03W4jn8+bJ3N8fLwm1Jl2zK+bFIO7djm3amjpGuY96DNUqlU9bK4NVYjAq40EF4urokFuiaup0m8bvFL4BaAVvgxIBcUCboLrLSgfrt/Vy3CVAi1hbX3Az/J8Sq+oBawWKl8IN5jL/yn1sVwurRfNeDy2rp3MMiJvzI3mmcGkAnu5XCKdTq/dsyo29cYIHUMQD86xqRLVuXQFsSt0OQYKjyAqSSk3l7pw6big583v7rpwBY6rwPS5cn6YQ88tWyORCCqVChKJBHK5nGVbjUYjFItFex7T6RRnZ2e4uLhAq9WyueY6U6tZx6G8Pv/PTB3m7nN8aiwwGK5zymQGnUc+16A4BkFjiedxhT7PEwS9Fp+xKnBXwWjmlpuW/DbBK4U3gPLqDI6ORiOzVLiQ3AVMq1+FkVqlepxrjfIF5MKlxe5a/G6mkWudukKSSoWKRVM9lRJSocT/cY+I0WiEbDaLQqGAXC63lmrIMequaAwkkmKigODeyxRSWjgHXAsNLXrSoKkqsyAajfOjSkEpPz47IJjK02fBZ+lmE+n88HhdMxrs5zFBvDjnjxSMphvP51etn4fDoVEy3G2OmW35fB537txBOp22LJ533nkHGxsbuHfvHl6+fIkXL17gRz/6Eer1Oi4vL20tAOueF++B+4BobGG5XNq6GQ6H6HQ69jy4Xpmhp+tZqR03SO/WmHAdut4hnxPnlM9cg/jumldlwvlWukzHNhwOX3lf30Z4pfAGIM3DrBYuRC4sLVjjglQKSReiQi0kNxDHz+oLose/rtGbKhwKQqWGqEg0F57Wtiojjp3nSCaT1p8ml8vZBvc8NwXf5eXlGsevKay8V90YJ8hLUAsvKGjP8fEcfKGZfaU0hs6p9m/iNTRepNldbtxF/6aCzRWAnHuNEwXFVdw1Rij3HY/HbV/vJ0+eIB6PIx6PI5VKmafGPlSFQsEKwP7pn/4JL1++xPb2Nvb29rC7u4vbt2/j1q1bODg4wKNHj/Czn/3MjBvtf8Txa3qy0pBKN7kdcLn29T51zvV5Eq5QV7jUUDQatS1LaVAsl0skk8lX4mVB3hg9E56Ta3gwGNgeHGQB3lZ4pfAGoADRHGb+XS13lzPl9yALNshddoULP+9aO191Hr0+x+ZSV68L4gYdHwqFrJKZweRoNGp1CipoaXVGIhGr52BQWGs5eF61DnnvmurL49x7VfpCU2ZvovPUs9P75Ln0f64Q1/Op0uGxvyj/7NJiHI/SgOoNArDtWqm0ksmkeWuZTMaURTKZxGAwwMnJCSaTCTKZDKrVKrLZLLa2tuw6zWbTKswJXYOvW1tKP6nydeeVCtSlWd3z8e9Bz5jjIo3KOJ16CcB6jyr3fEHeOP8/m81eMW7eZnil8AZg/juFHS0MBjf5s1tso5aTVoNqdbG7qDWnW4WNCil90VzrWgWLvmBKA7B3DY9R5caxa6CQwT62NMhmsxbEvLi4WAuWL5dXveiZlUSrDrhqiDYcDjEajWxegPWMIr68WnDnBm3V8tR+/i5nHUQtuVa+W0FNT4l/d1NF9bkE0UH6s0sl6X0GjSVIaKrHpUkNi8UCuVwOd+/etSaN0WgUGxsbWK1WKBQKuLy8xMuXLwEAt2/fxt7eHnK5HO7du4f9/X0kEgk8efIEP/rRj4xOo2eyXC7Xai40245j1bgWn5OuH45fn4EbjOff+GxVWarCUfqQGzfptrL0xrS7rdKFboxA427D4dBar9ykCN8meKXwhuBLQG+hUChYMzwVpi5FpPQLsJ7HDqwLe3fh3mRh0bpRq9v1CFQAuS+V6zG4PG8mkzE6gYqqWq2ubSLf7XZt8xQVIlSYFPD9ft9oqG63i/Pzc9TrdSyXS6tpcDOzdA4oNLSyVL0Cl3pyg5au5cg5Ui9Dn4PCLYgKivvoz2ohc+zuc+aY3HNS+LkKRBURe0nt7e0BAMrlsm1sE4/HMZlMkE6nsbm5iU8++QSff/45zs7OLEMsl8thtbqqSM/lcvijP/ojbG9vIx6P42c/+xm63a4VNNKDU0NF50/HxTXH56DFem/S9iXonnUuuGbpCRUKBaPPut0u+v2+1cAwU41rnu8sW7S71+/1erbDHb3etx1eKbwh1GKaz+fI5XLWnRJY5z6D3E+1Kl3loR4Fi4RoLbltM9xAs35ehZJaWFqnoJ4EX1hXSPEY9tZn0z9y2FQYbIymBWer1fUOYIxh8NrT6RTD4RC9Xg/z+RyZTAbhcHhtq0mlZwjNlafg0TgIx68emM5N0Fzpud3P8HnyHm7KdrmpijYIQWvC9ThcuozH6H1EIlcNCYvFIsrlMgqFgmV7MREinU5jb28P9Xrd5pkFhfQQI5GIeRW9Xg+NRgMA1oSnGzcJ4vt1zBpPc72KoPkJoorc9+ImYwa4Nni0PoMxF/7MvU64N7V6Z8vlEv1+39pbvI2FakHwSuENQQHd7XaRyWSwsbGxRncAN7v9FDpBC9r9jPL6q9X6HsQALPtJA9i6EQlfeAaiGSB2s2BWq5XtXcu/LxZXPYzq9bq1QN7Z2cH29jay2azRFsCV8Oj3+2bBMfWRSmw0Ghn/yxeUL24qlbLOrEGdVpUW40tMD00pJ1UGSj+4tBqP13ly6Tg+K43lKL+sBYn8v0vPBa0Zd6w6XlWemsGmComWdyh0tQUs5+6TTz5BrVZDNpu1cZCqi0ajeP/995FOp1Gv15FKpVAsFo0amkwmaDQayGazFogOhUJ4+PAh/vqv/9rmQIPkrrfLedZ0Vh2zJkIEeU+aPOBScTQwqAgSiYSdg14BvReuOY4vHo+jVCohk8kgm83i5OTEPqPPkimo5+fnmEwmryj4txleKbwhVCmk02l7MUiZ0JJVGkE9C76wzARS6AunljotY1UWFJBabazCQ4UaP09rXMeqlBODbACsknO1umrZXK1WUa1W11Jch8MhBoPBWqaPClzmrVNpRSIRJBIJDIdDRCJXO9gB13EO0k8UjOotqLeksQL+31UIwKs0nhuX4TwSbqBXn4t+Xr2Om7wCnaebAqp6fj43VzFRsKqHOJ1O0W638ezZM3z66afmMXCNhUIhm3cAqFar5knQes7lcjYWbZGuac9K27jjpqGhXkHQnGqcyvV63HO6c+56xfQ8dL26xhMNEWYSURGy9kANonA4jH6/j06nY7v0edroGl4p/AJgEJUbcCj3qRk2KsCBVzdrcWMHq9Vqrdc8hbzrOXAM+hLpSxHkzlOJsPaAwUO+aKrQaPWzApncs1qjy+XSUvfUqtOUVuB6bwcWNanQp/fAc7ISWZWdeg0aF3ChwkPnyf1dj3c9NlUGrmXrKiM9jx7j8uBKI+o13fPr+dTTUWudYG3A+fk5Li4urOeUrhO1hrPZ7JrBAcB6KU2nU6sqXywWRqPo2HTd3gQ3qO/+TxMGVKm6x+r43TXp1gGpoaRzS2+Sez4w1VaNB56X77F6tx5X8ErhF0S320UkEkG73cZqtbIqUmaFcOcoLYpiTrXLsXOPZwaO1TIiXIoJwJqAVSG4WCwsM4P51wTHRKHAnH6NWQAwV321WtleCLRYORbGBHK5nAmqVqtlymY0GlnuPDt8uhXgOm5ac5ovTy+Lc0Gr1fXGVGi7HoP+T++T13apGoXrsbmBZaU1tOBJBakKTKVelNN2M2/0Hgkezyy3i4sL/OVf/iUODw8xHo/x4MEDFAoFJBIJmxtSK+4zHgwGaDQaOD09xZdffol2u416vY56vW5JAxyLcuwaM3NTkN116yoCtfJ5f6ocXUWuz1itfFJuPCczpjSmwHdtOp2i2Wxaqi57Na1WKxwcHKDT6eDy8tKaMb5N221+FbxS+AVBy3s8HtsiZOWn0gDkLSkQgWthEpRLTbpAA2rk5NVrcPlZfs1mMxPG0WgU+Xx+jeedz+cWRKTVpbyuKjG+eMlkck1hkV9mnCIej5tA1/40AKyFdi6XCyxQU4tQrVnOlaa5upQYx0y4wtv1Bgj+ndfT+dNnp9Yxn6MKaV6HlIt7DRdK2blQizhIMWlwVBXVycmJeWutVgulUgnpdNoEuwZPtS15v99Ht9tFo9FAq9WytiVs6hgUdNc5d9diUJaQSxnpveu5VJnoPKin7a4XFd6aJut6BbwGvZ98Pm99mnq9nrXt5v14XMMrhV8QXETcGJ5CQyuWVYjpi0xB7HKorpWpewCoUnAFogpDUgDAtWCnBUXBHgqFrE8RPRtSRprays8qpaW9bDTjA4B5KMvl0gqL8vk8ksmk7WettQWcR96L6wXwi8JFFazOm1I+SksonadQYaHKVK17KiJVDPzdVQz6nBRBAt79P8fhWs+uB6RKSeek2Wxau4vxeIxyuYxMJmOplazwXiwWGAwGZj2zTmQwGKytTbfZIsehXo077yr8VTm4MRI3VqN0jp7TVZzqwSmlpv/X50EwfhcKhcxQoiEzGo3Q6XTWMuc81uGVwi8BWhv5fN6sMy5YfTm0URtfDLWy3JdjtVrZngRuvIK0j0trMB5BZdPv963VciaTMcFFOoGZFqx8pedD5REOX7VmplvNF4o8rN4rLbBkMol0Om0bzofDYct2CYVC6HQ6ZrUqt0xrW+dD+z1p4FopHyJI8OvfXeHE83BeNSMrKFajdSIaFOZY+RzoablBWBXi+qUcv256w+u6XhDvRzd5YkLAxcUF6vX6WnaUZqupIaJKn8/GFdjuPOhc8N6CnoEqC3qUSge5x6qXqseR7nIp1CBPgtfiffH9mkwmRl1y3XLznHa7jc8++8w84cFg8Mr6edvhlcIvCA1UqYAJCnBSyKpFq8IIWG+voC+cm3niuuGum75arWwjePUC3IwPUgvanmIymVgPHY6Fx9OroBKhcOLn6GHwJVRBrrvCESpwVCi58QZXMAZRLzcFQIOEGudR//Y66PNU2smde31mVGocg3sPN42P53S9E11XwHo2kF5nuVza71yPSmvpOgwKrOsYgyx2HY87rpuewU0eFMeniQbqFWkFPMeoBoKuDfWi3JRSnRNeo16vo9VqrZ3L41V4pfBLQJWCChp9qdVK1AwKAGtWmioDTb2kSw8EN4XTYB4/E4/HjcJh7YArmCioad2ScwZgFBDHzftjoI41EpPJxBqI6d4KVArks8n/ullYHIsrKDUI6wpwtTY5/iDh5Fr+LqXH87nXUQHlxi60mFCtfTeWAaxX8KqyUOvZFVbu8foM9P5VONLj0w2FaKTQE1Bv0p1Lt/+UPgf9jBsvoGB3M+OCPqP35dKOqhQ0UK9rnYFkl17SNGRgvSZI70O76y6XS1xeXqLVagHA2l4OHuvwSuEXBBdir9dDv9+37AZ1sQkVTlpARkWgqXb6MmoxDi0nFnqp16DWKtsrK0WihWEae2C6HrN7ZrOZba3InbQAWF8hWmOsambLZmZ48PyJRMJy4nluCi96MQSFEu+VsRMtUnOpMgpf9YBcisINet9Uj6CKwX1erqeiwkPjLUpzcDzqHSmN5FIoOia1vpWC4XrjmBgX0hiMFtOtVsE7hamF7noSNAx0Pl2rO0gJcn5UOWhKravEgxQe9+Lgc+dXJBIx6pOxkJvug1Qr1+h0Ol2jRbmuRqMR+v0+RqORPTuvFILhlcIvCGYOkZ+fTCaWFaHC3s044ovGFzAajSKdTtvLommZvAYXtmtN6svBl1ChXgg/4wo7FboUJrT41erli8pg8WQyQSwWw3Q6xWg0MgtWeWoqO90rQSkzAMZzqwBxC7/UolYaTflqhTsPLm3HeVSr9qtoEPd/On9usZbeg/7NVQQudehSOTpWjWe4Xgznwa3jcGknRZBn9TqFEET3BcVx9PyuQnCvzXVNz4GZbIvFAt1u1wS8xsMUGlegguMcuZ4a52c4HJoH+7r58fBK4ReG9voZDocYj8fIZDKmJACYICUfT2uGgjSXyyGdTltny/l8jnq9jvF4bEqAX9r4jpaydjx1BT5fGK0SdtNF+bMKYGarsNdRKBRaS2tst9vo9/umAHhu0lbaEZU1EEozMEhKaL2Em/mkgoUvORVgUDsJel9BysIVtK5icCkQHhukSPW4oOA3PTRdK248whXgmo7J8+mmM6p01ejgPGhMi/GkoPnhPbtejxtrcYPGHCuvoc9APQf+7MZdCJdmU69Wq66ZIcUUU90a1RX6PIaKheuO46QhMhqNcHl5uZaa63EzvFL4JbFaXZXV93o9ayWtC5lUDC32ZDKJarWKra0t7O7u2iLudDqWVhiJRKxLJV80TeNkzxcKMzcTQzdFUaFHPpvjpgeg/WA4/kajsUaFqNBRy4vn4Lac4fBVxhFf0kQiYXUKw+HQqkeZH67Ckvnlq9XKPJLV6jrIHRS0deMRvE+1gNWCdKkhVVg6FpdWel1AUufU9Q44/qAxKoLqHKhYVKGponodqBw1MUENBlcx85w0RtS75BwqJaTxCKWNXE9Qj9HYAb1Jzn+v10M4HEahUECpVMLW1haGwyGazSZGoxFWq5U1TNT1TG9DkyKosJR+HQwG6HQ6aLVaZrRwtziPYHil8EtCA6ouR+wGhcPhq83sK5UK7t69i0KhAADWxZLCV61wl1vXYLSeWwOS+jMQzJ1/Fe2i/DLPwfvVnHEKagoVWniMffC+U6nUK0FBzZShV8Pf9TrKHbvjdoWjcvsuleIqT70v92edb36eStWlQ4IEdBCN49JkKij1eKXHOL+up6KKIYjG4f271r47L0H3496bfsY9VsfvKgVVkOoduL+rsuAX95Xmfes2oUHrQD093qd6QOPxeK3/ka9L+Gp4pfBLgIKMqZ1MAaVQ58JlkDWZTOLDDz/E3t4e3nnnHTQaDdTrdRwfH5tFpAFXLnBa1LwmuXk3kMeXhdapWmLL5dICy0rBMACnFr/GBfS6qqQAWOCbLTUAWLM2FQCxWAy1Wu0Vy46KlMqBHVW1iE6LAV2hRiEdFBtwKRS9F02X1f8rFcSfVbFTMTGjR8/hKhsNEHMeXIWgVq0GZ6k8ScHpODlf6iVyfIQGYbVewoUqHq6roAwxjtf1MoKUtusp0PNUS17PCVwH7ZmoMR6PUSwWkc1mUavVcHx8jE6ng3a7bddkWxkGqrUZIL9YY0PPk4V6/X7fK4U3gFcKvwKYmgnglYAZX7RKpYIPP/wQ+/v7iEajePjwoS127sdAAUiBplyyctg3Bdz4QlJwqEBXS5PHawsJChDgOrdbrU19gSlEstksUqmUxVKoIFk4p7t4AescOTN3tMcROXK2PeZYVWArn67UmAoDgvOhtIe22XB5dbUgOecuhQTAlLJa4hSIah3z2tqFlOPTNGFVDuFw2LK3WBDJ6ltNP1blwmCsGgDAq94OP6vj0/WgCk4D5tp2BbhWsEoxuZ6pxih4rK4hrbRnivP29raNbTQaIZFIYHt7G6lUCt1uF8fHxxgMBmaE8dlrlbzGqAqFAlarqzhWt9vFYDB4rVfksQ6vFH4FkCPnoqSlS2ERi8WQz+exsbFhGTvsOcNgmmttuS5/EH/O/+tLz4BvEAd+Ex3El5reBc+pQovX4otMJcYqau0f46YVLhYLCygC10FjTZVVoaiZRbwW51FjJV8F3oN6BqoQ9L50Tokg/p7Xd7NzvmocLlyKRe9bj3Etb3d8+oyDKDL32u5aCTrGVVxqbKh34sZBblpzulbd8dIYoTEFrO80GA6HrSCSTRnpefO5Ku1Ixc53kcYJjZYgCtEjGF4p/ArQnddIu5yfn1uAtVqtYmNjA6VSCc1mE91uFycnJ2sUhyoCvoCJRML601xeXhr9A6xXs3Khq1ehL6gbQHU9B56DG+AA126/CivytDwvqafxeIxer2eKgJ/L5XKW2joajWzDeQbTeT4KAM2Zp0BXi1KvrZSEegjqVWnuvXoVamXzfmnJqmfgCkP1SNyxqnflcucu3aTenD5v/o3tSfr9vt2X2xeK4+P5gqxz/c771bhUkGJUGlJpLX5ev+vngjKPFJp6rCDdyuuxor7X65nx1O12sVwukc/nUalUAMD6OM1mM5yfn9s1GZSez+dIpVJ2Dq218XgzeKXwK4Lc5tbWFtLpNIbDofGa29vbtkENLWpSA6RbVOjy5UmlUkazEBQcLn1Ai1iFH3BtjfNLlUJQ1bBa68C6ENKXPhQKWYYUAGuX4VrVjEEMh0M7p1ZTU7BpBgkpFwoLrd7lNfQ6Kph0rG4Nh1JMOj8UkBo05/nca3DelLrQ+XOFuHse/TsFIj1L4GpDHK6Bi4uLNQUZdG6e3/Vc3DiEO46ge3M9FQ0cuwqF/9dzq7J0PV5VgppVpa3Um80mstmstWKfz+c4Pz9Hr9ezeyT1xPTnbDaLQqFgBsM//uM/otls2r7pvV4Pp6ena3Sqx5vBK4VfEavVVY/6dDqN/f19nJycmMDY3t62hctgq75MFIBKm1CQBbnkQXQGcG3laVthFYRqPapiCEo3dakml05SYa0uvDs2WuCs1wBebV4W5NnwOLX89RquxcljgubLhSsENS6gnoEiiEbSc+m5X0ctqUBUhU2BWSqVLB51cXFhHpsqAtfz4bO7SeiponTnQD9Dge/GDtykBtcbcNcW71Pv1VVCSp9RgQ2HQ4sv0OMcDAbmYQLXijSfz5tCLZVKa1vEzudzDIdD1Ot12wtcvSKPN4NXCr8iBoMB/vf//t/Y3d3FX/zFX+DTTz+1jInNzU3MZjPb8m84HKLT6RjlwhJ/AFY4FolErByfO7wx9uBWKBMs8AkK8Cm1wX5IbmYIsC60VOirsNNgqlrYQUKdP3NPBuXjdayafqjWvCvQXGuVtI0L9+88H2s8VBkoteMqHA0o67lY2BcOh62jpx6vn3d359NzADCaKJvN4sGDB8abn56eGk2iSs8NJOuzINS40KJAwqWuOLcMoms6scaoJpPJ2rPgGDRozftOJpNre38rVchj3bRtes609HkNepO8p1arZZ7Chx9+aOeo1WoWR7i4uLD9IdyOAB5fDa8Ufg1YLpdot9u4uLjAt7/9bQuchUIhy4Bg2iktKVfgqHJgoznSLcB6uwOlDWi5K7+sNQHAteDlZkD8G8+rgtQt6KI1q4KNikotTJdm4djU2tQYB4WEWumaRaPXCOL9Xej/lPd3Uxl1roF14aiZNjpG/k2buHEOVQi74D0pnaXxm1wuh83NTRSLRUQiEZydnaHRaKDT6awJfc6lrgO9hntPLoICyy4dpPEE9++r1WqNJtT/q3ekz9KNR+gx7tiYtUdjhc/MrU4Grp8pCz+p8IHr7CauFc188nhzeKXwa0Kz2cTBwQG+853vrAUxNUMJgMUKaE3zWN30g3sg86UAgttBu0qFL6NL+7jUFV9+tfIpuHWfXgaUlZsHsJZxRG9HBYjrZag1q5a6Sx2pB0QBpFa8+4KrQOIxKuR5PjewrUpP50QD23x+pK4ocIK8K6WdOAekftyAPSmiSORqD+WdnR0Ui0UsFgvU63WcnJysbQyjylSvoUYB14IKbVf5quflemou9eMqao0raIaP6925CkGhnpKulVAoZM0i1WsDsDb3upY5lk6nY8Vp+v7w/69TlB43wyuFXxP+z//5P/ibv/kbHBwcWErcRx99hFQqhVwuh3A4bNlEi8XCdsdiu4nBYGDCWxc1M2mY++9azBQ8aq2qZUiBTSGiLYPd3jd8Sd39akkvKCgktfsrhSmzaFwu3o0p8EvpJu395CoSYJ3+oOVOITOfzy2PnQ0HVSHzPLo3RKvVWiuY41iDWlQAWKOPlH5SQegGs8PhMNLptAnCWCyGcrmM7373u9bv5/PPP7dsmXK5bOtCC7N03t3nozEGvVeuIRXc7jMJqsDn/GrchVDhT2oHwJpwd+cCgNWncF5oILHAke8AjSH1dNn9lZ7WdDrFs2fP0Gq10Ol00Ol0MBgM0Ov1Xlu45/HV8Erh14T5/Gr/25/85Ce4c+cObt++jXK5DOAq7sCXf7m83lCdrSE0pVMtJQpjvhxB1o82R6OwCrIk3RebwkKFG+EWS4VCIcuEcqkDVUQaXKawd3/XzCK1PF2un/cf5B3ofWqwm1anVoKzxuEmIaFZSK7A5Hc3CKtzvVxep54C19YthZ6OlfO0u7uLvb093L9/HycnJ6jX6zg7O8NoNAJw3bLczXBSBNFG+nxVwLv/0/tzz6efU3qMCljnIxwOv2IscExKA+mY9PzMJOIzcgsGqejcjCr1aofDIQaDgcXtwuGwvUsevxy8Uvg1YrFY4OHDh8jn8/jkk09QqVSsuRfTTBeLhVVmTiYTo4qA69x3XdCuS+xCA7B6jAo7WrIq6PjlBpVdAUjLTwUBsC40qFQ4flVwpGC0zoKCQTtWUsnoPQa92MrJK2Uyn8+t7YYqOyozbRGhiohUXpCyVQrsdZSLS4lpPCaI+tnd3cX+/j5u3bqFo6MjtNttnJ+f272xp9ZisTBr3X3eqrh1nm5SDK5RwP/rs1baSek0KgU1NPjd9eoINRLccXFMsVgM6XTa1phWq7uJBvyMPhu+R6zpobfKmgWPXw5eKfwasVwu8fLlS9y5cwfdbhfdbhfD4RCtVssW72QyQbvdtuwSFZYUWHSV3Rfe5d95jH5XCogvIgvrAKwJwKA4BRWA631o7rpavcPh0F5iUj9KP2nATzONVCHpdx1LULouX/rFYoFbt25hd3cXtVoNX3zxhW18lM1mLWtFqS0qIa2EJZVFj0v/Rs9DrXI3NsC9JlR4qmfHXfCm0ymy2Sy2trZw79495PN5PHr0CF988QUODw/Ng9RnBqwbBbTWNfisSkfrWAi1zl2vkmNUa1y9RnY1ZQoo14HGebTCmNSarlWN+1BB65rgOzMej601uwaKXWgiQqvVst5G9FpIH3r88vBK4deMxWKB8/NzfP7556hWq0ilUqhWq+h2u+j3+6jX6xiNRq+0/dV9E5Q3D0IQJaACy6Vi1DrXF0Ypk9fRC2o98ktjHvRGghq2uYIk6J702JvoLh0DFed4PEYqlcLe3h5yuRzOzs7w4sWLtewd3ov2e9LYAUGFQIGk88ixq2Lks9ZMK9cy5xyzC24mk8HW1hai0Sj6/T6ePXuGo6MjNBqNNQGoGUCuAtAAsP5f50ifpz57d85da9xdG7onCNujh0KhVza757kSiYTNF5+5W3Cp90HK1R0D50yTIDj3yWQSg8HAGjDSQNB4nMevBq8UvgYcHx9jPB6jVqvhvffew0cffYTPP/8c3W4XR0dHtvDVamZetWbIqGWnFjqpIheuYlAFw88FHe/GIAgVKKoQ+Fla3bwOj1eLWwOlqgSDKBcVaFQKQXw/r9ftdhGNRrG7u4s/+IM/wLNnzxCJRPCzn/3MKDnl+nk+l8NWj4TUjXoIDPIzj1+rsXkd/o/ndBVHMplEoVDArVu3sFpdbSL/z//8zzg+PrYqeH6WKc2cK6Wr6KWQanOVt84ZP6/PXwWzBv31M8RkMkE8HkcsFkMul7PPsOuoZkZFo1GkUil7zkzB1piYPncmY9Cy5+ZOXFOaPk2PhXM4GAwwGAzQaDRsDjR92+NXg1cKXwPY0fH//b//h1arhXw+bzGETqdjm9EwjY4pfvyZPDIFO5WE24rCpVyC+vKo4qH1pdkxANYqazkWl1bQDXc0V1wFiZs6SDDLhGPmWIIsWN6j9juisKAF+vHHH6NcLlsw9sc//jH+v//v/0OlUsH3vvc9DIdDnJ2d4fz83M6rLUPcuXF3qUulUmuxmo8//hjZbBbJZBJnZ2dWZEVw7ubzq82UyL93u13E43FkMhl873vfQ6lUQjQaxU9+8hOcnp7i/Pwc0WgU+XzePh8U2wGwprw19qJej2ZBuUKe86k1H+6xy+VVfyrSjePx2O6lVquZJ9XpdGwtaW8mpZ1oLGgMiDGSRCKBZDJpHjPjCsBVQR8LOYlQKIRCoWA9jZrNJi4uLkw5efx64ZXC1wC63s1mE8fHx3j8+LG9yCpwVSi6gUO13m+Cfl69B+Xp1Qp0s3luyshxG7xxfK4Q5RfHqrSHKgge73o4tDCDPAzNbNG5Ig1TLpeNZuj1emg0GkilUsjn89je3sZqddV3qd/vrwWVOafsnqneEu+bP/M69+7dsxYMzWZzzQPSlhXszcN75lg3NjZQKBQQDofRaDRwenqKer3+StaVCnKly3Su2KGWFcZujMddTxqD4NrQ87vrSNcJnzWz5nivGk9RuAkN7jrjM+DeHUrFqSJRD1U9o+l0isPDQ9TrdXuuHr9+eKXwNYFu9vPnzzGdTvHd734Xy+USuVzO9mLWrQkpHFerlQWaQ6EQUqkUgGtlEUQJaRonr638qnoBqmjcTBzl8/X8Lu2k1+c5w+GwbXcYDoeRzWaNTmm1WsZDu56NZteoZ+LOpWbG0PouFAq4vLzE5eUlnj9/jq2tLezt7eG9995DJpPBaDTC8+fPjTrKZDJIJpNIJpM2JrZj0HbenM9isYhPP/0U3/nOd7BYLPD8+XNTjuTbY7GY0U1MsRwMBhiNRkilUqjVarh//z7S6TQ6nQ6+/PJLvHjxwnap07iMwqXrwuGr+goqHtZf6NrR+AYVCM/PeVQqLii+Q+ue4HUYzE0mk0in0xYD0/XEwkdV8hrY5z4c9MSUMmKRJ8eva4Sp3c1mE3/7t3/7ypg9fr0Ird7Q/wpy9T1uRjgcxre//W3M53O0Wi18+umnyOVyiMVitok4X/BwOGwvBABrI0xqgVkV3W7XOOrBYPBK0dVNudmukNfv/F9Q7EEte81aUWtW96OmQohEItje3kYymbRePt1u1xSY1mPQ4qQQ4bV0DwVa8NFoFIVCwSx+8taLxQLVahWlUgkbGxvY2Ngwz4xB3Fgsho2NDaRSKSwWC3z++ec4PDzE06dP14LnvK+dnR289957+JM/+RMcHx/j7OwMX3zxBcrlMubzOQ4ODmw/aS0mm06nKJVKqNVq+LM/+zPbSexv//Zv8fLlS3z55ZdrVFpQKwYaCrxnKqlarWbKiy3YdbOi6XSKRCJha4fzzNbRy+VyrYeRCn+3KJDPgEqkVCohmUxaBTuz6YIyhNz7SyaTyGQyePDggcVjnjx5Yu1ceJybwsr2H8vlEv/6r/+K8/NztNvtwDXu8WZ4E3HvPYWvCQwe0wqq1+tYLBao1WprFINm2XCze/aO598YjwDW3X7+HpRhEmThA8EcdNBC4d/5PzeASkGqtJEG0LmxCXljNwjOug31DjRIqvSOUigUckwF5T2RA2c2S6FQQKVSMb6aVFAsFsNgMLBK5n6/b7GJ6XRq1mupVEI6nbagcKPRwHA4xO3btxGLxWxrx+FwuJaZA8A8lkKhgEjkqsEhN1dSnp4UFOdXM7w4T/SSeA+hUGgtqKqZYHw+DA6z5bgKWvUGNd6kUPqS52UMgZ6VW1OjSpXpqcwWSqfT1uqalJTWJgDXtBWpOL4Li8UCnU4HjUbDK4TfELxS+JqwWq3ws5/9DNlsFtVqFS9fvkS73TZrd7FY4OzszHL7E4kEqtUqtra28N5776Hf71tRE7dlHI1Ga9QAr+PmjVOosJhLXXxSFqymVgFwUwWqKgdyy5pFQkFFq325XFp1rnb7TKfTSCaTSKVS2NnZQbVatf0m6A2FQiG02218+eWXODs7w3Q6RSqVMvqM+/XSWiV10ul0cHl5ifl8jna7jb29PSQSCdy/fx+1Wg23bt0CcCXcTk5ObM/sd999F0dHR9aZlFY+henDhw9xcHCA6XSKra0tPHjwAOl0Gvl8Hg8fPkSz2bTfi8Uibt26ha2tLVQqFfzkJz/BxcUFTk5OcHR0ZJQS74WKE4DRUZppFAqFMJlMrFVKrVbDaDQyZUh+X+ta0um0zTMDwi495SqioLWrcSXGULQynSCFxKJBXofeQbVatf286dlQ8XL7Vq7h+XyOcrmMfD6PWq2GdruNer2Ox48fv5IG6/H1wSuFrxmxWAzZbNZ2Xnv48CHu37+PfD6Pra0t6z5JqyoUCll2xfn5OZrNpr2E5GhJHQGvduIkaC0ysMcXvFQq2TlIJWgKKLBeSMff3WAkFQJpCv18OBw2a1bpLQYsWYkaj8exv7+P27dvm9VdqVTQbreRSCSQz+ethxK7xirNlslk1mIgvA4tSlq2hUIBT58+xenpKdrtNo6OjkyQVSoVFAoFlMtlDIdDE34HBwe2C5pa2r1ez3bV+/DDD5HNZrG5uYnhcIjpdGrzPp1O8eWXX+L4+Nh2CKPVz2fHQjcaBerxzOfXHXVrtRru3r2LfD6P09NTy3xylTGpJs6NVv7yOvTE3DoHPnM3BZbXIa1E708zuIBXFQxTrOnZLZdLZDIZ3L17FxsbG/j0009xcXGBs7MztNttxGIxq+lZLBbo9Xp49OgRzs/PcX5+bp6yx9cPrxS+ZjDQR7e53W6j3W7by874QTweRzabRTwet2M6nY7lsNPddl12/a6ZPMzrZvoflUOtVjMe16WCgPXWEeohqMLQOISmY6owUYrCzdkHYIFe5vBznvL5PFarFXK5HIrFIqLRKIbDoSkwvb6bdklFNZ1OrVCwUCig1+shFArh4OAAzWYTl5eX5rHMZjPs7u6iUCgAgM07v3e7XaO6VqsVRqMRMpkM4vE4tre3sbGxgZ2dHeO7XSqMiknTf1XJ837U81NBm81mUalUcOfOHcznc9Tr9bU9h3UbVC2u0ywf9fgIl27UzDVdu64nofel60bpQdJ8PBc90tFohEqlglKphK2trbVzMXkgnU6j2+2i0Wjg4uICjUbDvE6P3wy8UviawQ6Ot27dsq04Dw4OUK/XEY/H8f3vfx937tzBzs6O5db/0z/9k2V9pFIp24CELyKLhFjZy5eRllw0GkWxWDQqgS8iqZGTkxP89Kc/NQ8hFouZdQqsCyYVwIQGjN1KXP7fDWQTzNKhN3R4eIhUKmUB+eFwiOFwiEajYfx4u91Gv9+3Hbp4bVUS9FqYLsl4wcnJCZbLJS4vL9FsNjEej5FIJNButzGfz/HixQt8+OGHuHfvHgaDAV6+fGmdbsPhMMrl8prg6/V6RnOl02lTdM+fP8f5+TlyuRzee+89bG1t4T/8h/+AL7/8Ej/96U/x+PFjo2Q4x6pUmUI7n8+RTqdtPvf39/Gd73wHf/zHf4yf/exnVntBS50Kh1XHqVTKgsFuPEjjUW62mhvL4DGcY8aGNEtOP6+/0wuhQiTlSS84FArh9u3btn85KaRYLIYnT57gxYsX+OlPf2qxI4/fLLxS+A2BAVkKwNlshmfPniGZTKLb7aJYLFpOO7nkwWBg1a36MqpwYQ63BpzD4TAKhQJqtZopg3K5jHK5jHQ6jV6vZy+xBkj5uzYmC8qBJ1zLkwJFC6yU7uELzu+TyQS9Xg+LxQLxeByVSgWJRAKr1QqlUsms4k6nY/SIZi0B171wmAVEqmw+n9sOd0yp1EwhBjKBK+/g2bNnViXNe9P21Rr8JG3DVhuz2Qz5fN5aiU8mEzx58gTPnj3D4eEhXrx4YcJRqTUKcw24UqFTQTCzq1wuo1qtYmNjA9vb22g2m5hMJmv1CJlMBvl83jwvCmPGkZQicilBNQI0G4s0IuM+bn8q/ZnPlbEDd20tFgtr80KKcLVa4eXLl+h2u+j1ejg+PrasLiZqePxm4ZXCbwia000qotVq4eXLl1itVnj//fdRKBSQTqctc0PrDdzMEbrr2qJB2zbE43Gk02kUCgXk83njzfn/oP5K7kus1JEqBQoLrW/gcfyu1IDOAa/L+6NQ1TRUWuH0SIbDod2r0hJaVa33weyu6XRqtJMGdamEeH4qDFVinGNmJWlNhcu301OhAm82mxgOh3j27BnOzs7QarUAXCt2BedKFQVpLQCW159MJi2Dp1wuv1K85VJGpOb0GbsFcjoG19rXZ+bipvNpfMKNS/D/pLu63a4Fn8/OznB5eWlZXqTDdO8Pj98cvFL4DeHo6Mh+vnPnDnK5HCKRCE5PT3F6eopGo4Hvf//7eOedd/Dxxx/j+PjYsmIYGEyn02u5/ZrnTl4WuOL3Ly4ujMcdjUYIh8PIZDJot9vWw195bAppFQyab64K4KZ+SgwGajrlZDKxz1GwMg+eewBfXl4aRUGlmMvlTEi3221kMpm1HesYdFfFQ8FOgaQ8urZjoCKi8iRlpcFTKpFoNGqpoKvVCv1+H7FYDJubm7h37x5u3bplXt/h4SG++OIL8yB0LpLJJFarq4JGzRBjqiepQh5XLpeRSqXwwQcfoFgsot/vI5FIoFwu4/bt2xasZ6yKcSRuWN9oNOy8QfsUawyJgl3jX3xmfKZB1CDnnOuGmW5asazxBc5DMpnEixcv0Gg0cH5+jp/85CdGl3I/DHpsHr95eKXwG4K+lM1m0zZtz2QyiEajeP78OcLhME5OTlAsFgEAtVrNCtsYbA2qFyDUqtId3chdU1nQkiW0nYSeW4O6qhgIDTjq33RsugGNtr6gsC2Xy5Z51Gg0kMvlrD6AmVnMzKGQ5r2qEKfQZmbV66CWLqkh/d09N++bmUWpVAqlUgnL5RKtVgsnJyc4ODjAycmJCTfXK9DMHq3ipvcTj8eRy+WsuK5QKKBareL27dvI5/Nr6aBM36VSYEJBqVSy84XDYfR6PQvSKiWk9+s+OzctVhUGP6PPWGsrdO3xWK11oIIYjUZ49OiRBZMZr2B85qYiTI/fDLxS+C1ArSAKvNPTU0wmE5ydneH+/fuoVCooFouo1Wro9/sIh8NoNptrwkUpFQ0sUnjQmuRLzZYT7XYb4/HYhINSSfoCa0aKpq7yWM00UutS6yLYDZP0GYUJU3WZCjoYDCz9lnQSx8W4Cc+rdRmu4GXwlX9XAUhhxlhCUCYTFQPH6bb0SCQSyGQyKBaL1gvpyZMnODk5QaPRsLEnEolX9pVQikctfCq8bDaLTCaDZrOJZDKJcrmMra2ttYaA2kVUlUI8Hre4BsfKLqRBXgGfMeeBv2v/KoLCXJ+1piXzvFqnoDSfridWV5+dnaHZbKLVahn1qS0xPH578Erht4yTkxMAVy8x+yL94Ac/sIKlDz74AOVyGe+//z5ms5lZ1PV63do8M2CZTCZNeJAOYXCVvPXR0REuLi6MPgHwClevwVQ3psEXWAOQSucA6wqFn6OVTUFeqVSQz+cRj8dxdHSEZrOJTqdjTdGYMRUOh41CI7XhpuXqGIBrSoP/VzqLGVtULEo/8VwU6vydgdvRaIRisYhqtYparYZGo4FGo4Gf/exnZuGSXmOQWHsQUSkAr7a0zmaz2N/fR6VSwbNnz2xuuDZUYan1v1gsLG2TgWaXz1djQTvlqmdJpctaGWC9ip3Xdg0S9f5oFKjBwAQB4IpeZGVys9lc21PE43cHXin8lqEvJtsGa0+Z58+fo9VqodlsWi+YYrFolayDwcC8ASoEvriugGM1LNtgqzAErl9+V6AD6wVsQVlH/Lz+HVjfU9kNBi+XS/T7ffT7fbuHcPiqKndra8s8Gpfe0tiHe17gmv/m/Lr0B6F0FM8TdD49b7FYtH5U5MS1Y6pCPSNSNRw7FQit/FKpZA3vONdK5ZCzZ/JAJpOxuWCGEpsQ8hqM2yiFo/PiPi83YYD37KayclxKgymFp8+Glc6z2Qz1et3Si1nQ5vG7B68UfodAAQlcW61ffvkl0uk0SqUS3nnnHRSLRWxublp+d6vVwsXFhVXU8uXUdgLstEqlwNoGFZaqJIJeVgo1V6gGZR/xfKoM9FiOjy0pyH3Tuua2lWxrQW8oKH6inoLSXPw7A76u96DN9/RLM770WqSdSqUSCoUCxuMxzs/PzYvR1iM6HuBaiVIR6NhTqZTFVhh412A6t+Bk3IFtU05PT+0Z5nK5NUqLGVcM+rrj4bMLgqak6jPU+9CGeQTpMRonvD/SaOPx2NapxsY8fvfglcLvKNh+WTNTfvjDHyISiSCVSmFjYwP5fB6bm5vY3NwEcCX0Ly8v0ev10Ov1rJo5n89jNBpZyh97KAHrfXCYwUSPQ/d5ViuYQisovZHQVFmNNfB4Wo3RaBTtdhuTycQ8mPF4jMePH+PJkyeo1+v2Wf5PrWCej7EZelCkh6hM3F5RCq0TIFTwkYJje4tUKmUtM3q93loMhRSe1h1wDAwQc1yFQgHFYhG5XA7JZBL9ft92k2MG1GKxMC9gMplY8sFgMLA2EbwneppUruTwWSOh41Q6ELhul6LWv3pX6gHomlHlwgyjbDZr1x8Oh1aZrO0saPx4/O7BK4XfUajwYlrmaDQygXFxcYFut4vxeGxChcHJfD6P5XKJnZ0d7OzsIJfLYTKZWM8a4LqdBS1KWtIUpjyG0DYbmvKpUE9A/89URbXmlU9m2mMqlTIKpNVqmTDLZrNrHLgb7OYx2lKbgkwrn1U5BY096BkoFcItOlkt7QZF3fNq91iC3hk9D9JHmirL+EWr1cLR0RE2NzdRq9WsUnk+n6NWq1nKL9uY0KtgEJt9h9wYgcJ91i59xmemKckKKpZ4PG6pqFTMw+EQl5eXtnbpWfg4wu82vFL4PQBrDQBYR87Ly0ssFgscHBxgc3MTlUoFDx48wNbWFnK5nHWarFarKJfLaLfba32UIpGICWPds1jpF02rVCtfeXgVNDy30gosxuJmNJohxIyT+XxuzdDK5TIikYhlpXCfBtJehI4rHA6jWq2a53J+fm5eDjNiKLw06KuUmUJpNY23ZDIZy+pptVprVItLH+m8uhXivHYqlbK6DCr+8Xhs/X+GwyF++tOf4v3337cCRCqe3d1dixWpsmMq8nA4tJ+135Lel5t5xbnUe+axSje6ypJ9tqrVKhKJBOLxOJ4/f45+v4/Dw0PrfZTNZi1+5PG7C68Ufs8wGo2snw9bQ/R6PfT7fVxcXGBjYwO1Wg1/8id/gnw+j93dXZRKJbTb7TWLm1adm81CK14Diq51GNRFU38n3cKNVW7fvo1cLoeXL1/i/Pwc9XrdAsi8Dre9/PDDD23/Xh77r//6rybkGNTlWGlxp9NpK3rSQC2VAMenqalBBV2Mdyi/z8LBarVqKbbcOS+Tyay1d9B0WU1tVWGazWaRz+dNkVEhsPcTGwPGYjH89Kc/xcnJCb788kv82Z/9mbUbXy6XtuEQOfrlcmmtPRqNhglgtdCDkgG4JuhZaBM9VZAKvVcmNMxmM/T7fVxeXuLp06fmIbDSWxsGevzuwiuF3zNQaAOw/jD8fbW62hBmOp3iyZMnJhDv37+P5XKJfD6PVCq1lt7pBhGVaw6iC9y/qQDh/8PhsLVjePDgAXZ3d23T9eFwaIKb12MLh0qlYjTJeDy2lgfaVI3xDComctS0htUbUuiYtUiL1BkFps4J50VTOBlv0etyq0r3WkpvkX9PJpMolUqW2jqdTjEajdaCr1TamUzGOr4eHBygWq1iZ2fH5iCTyWB7exvD4dA2GOp2u5hMJqZEXQTFgTRzSNNQb4oZ8W9Uyow/URExxZiUEc/raaPfD3il8HuM2WyGy8tLADCvod1u4+zsDIeHh9jd3cXt27fxX//rf0WhUMDu7q5ZmY1GY22fBRXStCyVPgCwJmj5OQYmNQMnmUziww8/xP7+Pv7sz/7MgtfcLIi9gCg0Wb27u7uL7e1t29mMO5aNx2PLv282m2vUTyaTsZ3qxuMxOp2O9dSh1a2UGJULaZtsNotut4t+v78WN9GdxZbLpXWqZSolK69Jz7BxHufJTXVld1a2wa5UKtjb20Or1bLgPyklBrUZSzg5OcHh4SGOjo5w69YtfPLJJ/jOd75j248OBgN0u108efIEg8EAjUYDzWbTPDY3zRi49ub0eXJN0bNSha/eIY/hM59Op2i32zg8PES73cbp6amlQpdKJVxeXq5V0Hv8bsMrhW8IZrMZjo+PzdJLJBI4Pz9Hq9VCr9fD1tYWdnd3AcAoDwYjWeFLq5zCTYPKrlDRylWNNaTTadRqNWsHPh6PcXR0ZO0g2OKD1+COc7u7u9jf30c+n8dwOMTJyclaj6bhcBhYH5FKpZDP542qoADUwj3gyqpNJpOvtJXY3Ny0TqxaoetWI7OjK7fpJE3HNtzPnj3D6empFSMynkEPgedOJBJ4/vy57Q/NfQXI569WV+09Xrx4gcvLS9y+fRvpdBr37t3D5eUlGo0G/u7v/g7Hx8fWBiObzWI+n6/tA0HvSj2k1yUS8GdNJ3XbXWjGGg0BUlT9ft8UEYvoVqsVzs/P17YP9fjdh1cK3xBQmAAwocb0Rwq9fr9vTfVYLatBSGB9D2e3hQSVg7aO0M/yZ2bChMNX/XfIl/f7fYxGo7WOoLFYDLlczr5o2Q4GA6NFKGC1gG65vNrJi9k7el5NCeV8aCxhPr/aTD6Xy2FjYwPdbtfqIdzaAv7Mnj3/f3v3ttt2lYZx+LUTJ47rTTaOk7hpQ0w3QmqLQD0DTrkG7oFr4hKQKg444Q5AiEKLWgIKTYGk2TlxbKdOmtRzUL1fl00YOjPMaEp+j1QNTFPHScr6/mt9m+XP6aObcrkc9y/v7u4OVFhZWnF04cKFge5qaXCR9cf7ZjzfM10sFuOu7uFEt+912NnZiYq0tGfDnyP957NyRf77kB6hpYnsNAfhhrROp6Nut6t2ux0zp9J8DEnl1w9B4W/o+fPnA5ec+0hhdXVV8/PzcRe0RzT7jN93Bvj8Nx1zkS5g6bFDWubqj52ZmVGz2ZT0YuE/OjqKklifn7sqyb0WU1NTA2MdXF/v3Eda0eTE+LVr11QqlZTNZrW7uxuv6/ed7hrSuUnPnj3TzMyMrl69qps3b8YitrKyEkciPiLx4u67FhygXClVq9Xi/N7fxzQA+evp9Xox4bTRaCiXy+ng4CCO1NL7CpyD6PV62t7eVrVaVaFQ0JtvvjlQOtrr9WI8t4fKOTnsI610J+jjMQcF/0zTgoLhXEz6M/f3w+WmBwcH2t7eHhhmJ728JwOvJ4LCOeFFrtlsxgUx9XpdY2NjqlQq0VHs/8DdIJbuHKTBWUJeQNPmr0wmMzDAziOv3XDlXUoul4tcQqPR0MLCQvRaVCoVLS8vx93N6+vr0YPgu53Hx8c1Pz+vZ8+exeiOdPF3Ajs9tvHvz83N6fLly6rX65Es9eunpZZpGakbwTxhtt/vq16v6/j4OCbPuiHLC7cX5l6vp8nJSV28eFHXr1/XrVu3lM1m9csvv+j+/fs6ODiI4zwnnqUXi7VHmfjoy70cFy9ejJ6Me/fuRR4pzYP4Z5UG9rQkOe03SfNGaU+Ivxa/LzejeUrrWeND8HojKJwTPsLw5SxOlhaLxUhuSi+7laXf3+MrDY628Dm4nxCHnzxdQeP6eZ/TuxzWZZnz8/OxU/BQttnZWc3Pz6vT6Whzc3NgzIV3GJVKRc1mc+BCGb9vL3ppp67HZUxPT8d92O4HSK/FHK7fT5u//HVks1nt7OxEhZDLZdNub/+Z09PT2FUsLCxobm5O/X4/psKmHdgOPmmC2otxoVCICatuWJSk3377LW6n8xFYurNLj7PSgGDpUVJa6uu/Jz6C7Ha76nQ60TNyfHwcu6yz+j3weiIonDOenipJKysrcQWor+osl8vxH3yn04mjpPR4IX0Sln6fwMzlclFyOTc3FxNdvXNw09alS5fUaDTUaDTiSd59AdVqVbdv31a1WtXh4WHcr1wqldRoNFSv1zU5OamtrS3t7OwMvI+zgloakGq1mp4+farV1VWtrKxE1613AN5dSL8f8ud8wP7+fgSU6elpTU5OamJiQs1mM269c3NgPp/Xu+++q7feekuNRiOuCvXVlH7P/uVjNOlFbb+Pz/L5fEzKzefz8T32z+Xw8PDMYYD9fn+ghDbt8/DHpJ/fX6OrijqdzsAEW1etuaMafy8EhXPIi7PPwg8PD7W3txddxT7e8biMPzoa8NOkE8u+/2F5eVn1el0zMzORID0+Pta1a9eiHn98fDxKZsfHx2PH4SfPk5MTzc3NRU7i4OAgEpo+Nnny5In29/cHqlvSfoP0jPzk5CSuOXWi9tmzZ9ra2oqBfGnQGx7rMTz+wSW23W43jp6cEHZOpN9/cYOav06XzroIwEHXuZy01DOtWMrn87Gge3E/OTmJYOkhc8PdyunrpP0E6Yyj4WGAnU4nOqvTwoC0Mm04kY+/F4LCOeRFz0chTnj6XuRKpRLn8ekZfdr5OtzfkMvl4p6B6enpmNzp+T6lUkmXLl2KhWVsbCzGc5w1xjuTyWhycjLm+/siHt/n6/fsEQrpMVa6k/H7dlCQFBU8TvI6n+InYAeB4VEXfm0flzhf4DsDxsbGNDExEcP9stmsyuVy9CV4MXfVTqfTiQR/OojOwcmv6VyLj33GxsbiUnsf6aRBwdJKovTnd1ZHupPr7q72UEX3a3gwIAHh74+gcA55LEa/3496fd8itrOzE4uPyz09x99PlJ53UygUBo6QPMRtZWVF/X5f8/PzunbtWiSbfR7u45B+v69WqzVQpuk6f4+C9v3IrpZpt9t6+PChHj16pCdPngyMcE4rhcz9Cg4e/jpdTeP8R9qsdnx8HO8vPV5JewmOj4+jqspHcs6heMGdnZ3V7Oxs9IX4CXxtbU2tVku7u7vK5XKampqKm/BKpdJAXmNiYkKVSiXucHDPxOrqqg4ODtRsNlUsFmP35c9dKBQkvew9cXCp1+uxK/jxxx+jHNeBrNvtRl7Cg/oIBOcLQeGcShdQL5hejPx06fk+uVwu+gW86LiiJ329nZ2duK5yY2ND5XJZn332mW7cuKF6va6joyNduHBBhUJB3W5XGxsb+vXXX/X222+rVqtpcXFRzWZTBwcHarfbUZrZ6XQiGe3/9eykbrervb29KBf1KAzp5ZOy5zCVy2WVy2UtLi5GmejGxkY8lXs2VDq/SBqcieTvz8TERPQ87O3t6fT0VPl8XjMzMxEE/HouK/X324twtVpVLpeLqibvGjx/yMd8/v8ceMbHxyMQuuLJu6H0esw08V8oFOIOaO8GvOPqdruxg/MRliQuwjmnCArn3PPnz6MEcpgXivQazbRMMz1KcQmrj1/W19c1MjKib7/9Vh999JFu374dPQy+IOi7777T119/rX6/r5s3b2ppaUntdltra2v6/vvv4+m11+vp1q1bajQaWlxcVKVSiblPrVZLOzs7URE0nBhOK5HcW+BjrHa7rbGxsVgIC4VCLK5uuhoeruf8hSt/fBTkoFAoFAamk56cnGh7e1tLS0vKZDJRw5/NZlWr1aLM1rsuP6Wnx1HuDfDXljYHpncwuyAg/dk6r+CGu0wmE8dwm5ubkVdKG/fSP4/zh6CAf8rllmeVHGYymRgT4UvrvUA9fPgwPu7OnTv6/PPPo7JneM6Orxm9dOmS6vW6VldX9cknn+jjjz/W9evXNTY2psXFRU1PT0eyeHR0VIuLiyqVSlpaWtK9e/di9+CKHi+S3ilUq1VNTk5GrsKllj/88IN+/vlnPX78OO4FkF7upvzU7uMrSXFngJOx3mF4NMfe3p5qtZomJyc1MzMTM5/29/ejN8SNd8ViUR988IGOjo6iYc3fx62tLe3v72t/f18zMzNREeTkeDabHfj80suyUw9PTKu3vEvxL8pJMYyggD/1R4uG8whpj8BZ5/pudDpLNpvVgwcPYlT2yMiLK0i73a6++eYbPXnyJCqharWaPvzwQ3355ZdaXV3Ve++9p9HRUU1NTanRaETjnfsO0ms8fQTjG8Dq9boqlYreeOMNZbNZlUolbW9vR24hfUr2sY6vw8zlcpqeno4g5YR1q9XS+vp6BKOpqSmVy2Xl83nt7u5KevH0XavVVK1WVS6X43Ok+Q3vjp4+fRo7BHd+5/P52EU4rzN8Z4TzCOn0VO9e/OusxDQgERTwH2q1Wv/Rn3/+/Lnu3r2ru3fv6s6dOwO/98UXXwz8+8LCgi5evKhPP/1UX331la5cuaLp6Wnl83ktLy/HcVF694ITyoeHh3rw4IHW19f1008/6Z133tHVq1f1/vvvq1ar6cqVK3EHgM/3fVx2eHgYT/Szs7MqFouampqSpMgpbG9va21tTevr66pWq1pYWNDi4mI05K2trWl0dFRzc3NqNBqqVquxW/GOxY1upVIpRkl0u90YGthoNJTNZnV0dKStrS0dHR2pVCrF2X82m41Fv9lsRo9B2mldLBb/cCQ2IEmZ/ivuHc/qbgX+l0ZGRlSr1dRqtdTr9TQ7OztQXunFOs15+O7m0dHRSKpubm7GWOobN25odnZWU1NTMW3UiV7nFu7fv69Wq6V2u625ublIWruaaXt7W48ePYqJrr4XwsllVzwVi0VdvnxZS0tL0XfgiiGX1z59+jRGZvR6vThmqlQq0fPRarX0+PFj7e3tDeQG3A/iclvzMVHatMaR0fn0Kj93dgp4bZyenmpjYyP+fXNzc+D3JyYmYhCgg0Iul4ug4P6ETqejTCYTI7x9r3WlUomzeQeF4+NjPXr0KMp42+22JiYmIgGcdie32+0IQqenp3HTmKee5vN5dTqdKKXNZDLRuOfuYL+ej3darZby+XzkUnyvw+7ubkygdamrdxtOwqf9CdLvJ98CZ2GnAPyLhi+gcQdxukNJE8B/ZmRkJO5fdhLe+QVXBqX3XziHM7zIpzONGFmNs7zK30eCAvBv8D0G7gS29L7rf+Xc3jOLhuc2pSW/aRD4o2owY0eAs3B8BPyXpCM/XPnzZ4v2P/NXdAwTCPBXYKcAAOfEqyz32T/9CADAuUFQAAAEggIAIBAUAACBoAAACAQFAEAgKAAAAkEBABAICgCAQFAAAASCAgAgEBQAAIGgAAAIBAUAQCAoAAACQQEAEAgKAIBAUAAABIICACAQFAAAgaAAAAgEBQBAICgAAAJBAQAQCAoAgEBQAAAEggIAIBAUAACBoAAACAQFAEAgKAAAAkEBABAICgCAQFAAAASCAgAgEBQAAIGgAAAIBAUAQCAoAAACQQEAEAgKAIBAUAAABIICACAQFAAAgaAAAAgEBQBAICgAAAJBAQAQCAoAgEBQAAAEggIAIBAUAACBoAAACAQFAEAgKAAAAkEBABAICgCAQFAAAASCAgAgEBQAAIGgAAAIBAUAQCAoAAACQQEAEAgKAIBAUAAABIICACAQFAAAgaAAAAgEBQBAICgAAAJBAQAQCAoAgEBQAAAEggIAIBAUAACBoAAACAQFAEAgKAAAAkEBABAICgCAQFAAAITRV/3Afr//33wfAID/A+wUAACBoAAACAQFAEAgKAAAAkEBABAICgCAQFAAAASCAgAgEBQAAOEfhpWVFWxDtxkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nibabel as nib  # 导入nibabel库\n",
    "\n",
    "# 1. 加载nii文件（支持.nii和.nii.gz格式）\n",
    "file_path = \"BraTS2021_T1T2/imagesTr/BraTS2021_00000_t2.nii.gz\"  # 文件路径（替换为你的实际路径）\n",
    "nii_img = nib.load(file_path)  # 返回一个Nifti1Image对象，包含影像数据和头信息\n",
    "\n",
    "\n",
    "# 2. 提取影像数据（像素值）\n",
    "# get_fdata() 返回numpy数组（float64类型），支持3D/4D影像（如3D体素或带时间序列的4D数据）\n",
    "img_data = nii_img.get_fdata()  \n",
    "# 查看数据形状（例如3D影像可能是 (240, 240, 155)，对应x、y、z轴的体素数量）\n",
    "print(\"数据形状：\", img_data.shape)  \n",
    "\n",
    "\n",
    "# 3. 提取头文件信息（元数据）\n",
    "# 头文件包含影像的物理信息，如像素间距（mm）、坐标系、维度等\n",
    "header = nii_img.header  \n",
    "# 查看关键信息（例如像素间距）\n",
    "print(\"像素间距（mm）：\", header.get_zooms())  # 输出 (x间距, y间距, z间距)\n",
    "print(\"影像维度：\", header.get_data_shape())  # 与img_data.shape一致\n",
    "\n",
    "\n",
    "# 4. （可选）查看 affine矩阵（空间变换矩阵，描述影像体素与物理空间的映射关系）\n",
    "affine = nii_img.affine  \n",
    "print(\"空间变换矩阵：\\n\", affine)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设是3D影像，取z轴第80层（可根据实际shape调整索引）\n",
    "slice_idx = 80  \n",
    "slice_data = img_data[:, :, slice_idx]  # x-y平面的切片\n",
    "\n",
    "plt.imshow(slice_data, cmap=\"gray\")  # 医学影像常用灰度图\n",
    "plt.axis(\"off\")  # 关闭坐标轴\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cdal_cpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.23"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
